Links: https://link.springer.com/article/10.1007/s10458-023-09634-5

Bibtex: @article{Buzcu_AAMAS2024, title= {Towards interactive explanation-based Nutrition Virtual Coaching Systems}, volume= {38}, DOI= {10.1007/s10458-023-09634-5}, number= {1}, journal= {Autonomous Agents and Multi-Agent Systems}, author= {Buzcu, Berk and Tessa, Melissa and Tchappi, Igor and Najjar, Amro and Hulstijn, Joris and Calvaresi, Davide and Aydou{g}an, Reyhan}, year= {2024}, }

Abstract: The awareness about healthy lifestyles is increasing, opening to personalized intelligent health coaching applications. A demand for more than mere suggestions and mechanistic interactions has driven attention to nutrition virtual coaching systems (NVC) as a bridge between human–machine interaction and recommender, informative, persuasive, and argumentation systems. NVC can rely on data-driven opaque mechanisms. Therefore, it is crucial to enable NVC to explain their doing (i.e., engaging the user in discussions (via arguments) about dietary solutions/alternatives). By doing so, transparency, user acceptance, and engagement are expected to be boosted. This study focuses on NVC agents generating personalized food recommendations based on user-specific factors such as allergies, eating habits, lifestyles, and ingredient preferences. In particular, we propose a user-agent negotiation process entailing run-time feedback mechanisms to react to both recommendations and related explanations. Lastly, the study presents the findings obtained by the experiments conducted with multi-background participants to evaluate the acceptability and effectiveness of the proposed system. The results indicate that most participants value the opportunity to provide feedback and receive explanations for recommendations. Additionally, the users are fond of receiving information tailored to their needs. Furthermore, our interactive recommendation system performed better than the corresponding traditional recommendation system in terms of effectiveness regarding the number of agreements and rounds.

Cite As: Berk Buzcu, Melissa Tessa, Igor Tchappi, Amro Najjar, Joris Hulstijn, Davide Calvaresi, and Reyhan Aydogan,“Towards Interactive Explanation-based Nutrition Virtual Coaching Systems”, Autonomous Agents and Multi-Agent Systems, 38:5, 1-26, 2024

Topic: Explainable AI, Recommender systems, Interactive ,Nutrition virtual coach

Type: Journal

Links: https://dl.acm.org/doi/abs/10.1613/jair.1.14972

Bibtex: @article{Canturk_JAIR_2024, author= {Cant"{u}rk, Furkan and Varol, Taha and Aydou{g}an, Reyhan and "{O}zener, Okan "{O}rsan}, title= {Scalable Primal Heuristics Using Graph Neural Networks for Combinatorial Optimization}, year= {2024}, issue_date= {Sep 2024}, publisher= {AI Access Foundation}, address= {El Segundo, CA, USA}, volume= {80}, issn= {1076-9757}, url= {https://doi.org/10.1613/jair.1.14972}, doi= {10.1613/jair.1.14972}, abstract= {By examining the patterns of solutions obtained... [abstract truncated]}, journal= {J. Artif. Int. Res.}, numpages= {50} }

Abstract: By examining the patterns of solutions obtained for various instances, one can gain insights into the structure and behavior of combinatorial optimization (CO) problems and develop efficient algorithms for solving them. Machine learning techniques, especially Graph Neural Networks (GNNs), have shown promise in parametrizing and automating this laborious design process. The inductive bias of GNNs allows for learning solutions to mixed-integer programming (MIP) formulations of constrained CO problems with a relational representation of decision variables and constraints. The trained GNNs can be leveraged with primal heuristics to construct high-quality feasible solutions to CO problems quickly. However, current GNN-based end-to-end learning approaches have limitations for scalable training and generalization on larger-scale instances; therefore, they have been mostly evaluated over small-scale instances. Addressing this issue, our study builds on supervised learning of optimal solutions to the downscaled instances of given large-scale CO problems. We introduce several improvements on a recent GNN model for CO to generalize on instances of a larger scale than those used in training. We also propose a two-stage primal heuristic strategy based on uncertainty-quantification to automatically configure how solution search relies on the predicted decision values. Our models can generalize on 16x upscaled instances of commonly benchmarked five CO problems. Unlike the regressive performance of existing GNN-based CO approaches as the scale of problems increases, the CO pipelines using our models offer an incremental performance improvement relative to CPLEX. The proposed uncertainty-based primal heuristics provide 6-75% better optimality gap values and 45-99% better primal gap values for the 16x upscaled instances and brings immense speedup to obtain high-quality solutions. All these gains are achieved through a computationally efficient modeling approach without sacrificing solution quality.

Cite As: Furkan Cantürk, Taha Varol, Reyhan Aydoğan, and Okan Örsan Özener, “Scalable Primal Heuristics Using Graph Neural Networks for Combinatorial Optimization” Journal of Artificial Intelligence Research, 80, 2024.

Topic: Combinatorial optimization, Mixed discrete-continuous optimization, Neural networks, Discrete space search

Type: Journal

Links: https://doi.org/10.1093/ptep/ptae106

Bibtex: @article{Simsek_PTEP_2024, author= {c{S}imc{s}ek, Ebru and Ic{s}i ldak, Bora and Dou{g}ru, Ani{}l and Aydou{g}an, Reyhan and Bayrak, Burak and Ertekin, Seyda}, title= {CALPAGAN: Calorimetry for Particles Using Generative Adversarial Networks}, journal= {Progress of Theoretical and Experimental Physics}, volume= {2024}, number= {8}, pages= {083C01}, year= {2024}, abstract= {In this study, a novel approach is demonstrated... [abstract truncated]}, issn= {2050-3911}, doi= {10.1093/ptep/ptae106}, url= {https://doi.org/10.1093/ptep/ptae106}, eprint= {https://academic.oup.com/ptep/article-pdf/2024/8/083C01/58746039/ptae106.pdf} }

Abstract: In this study, a novel approach is demonstrated for converting calorimeter images from fast simulations to those akin to comprehensive full simulations, utilizing conditional Generative Adversarial Networks (GANs). The concept of Pix2pix is tailored for CALPAGAN, where images from fast simulations serve as the basis (condition) for generating outputs that closely resemble those from detailed simulations. The findings indicate a strong correlation between the generated images and those from full simulations, especially in terms of key observables like jet transverse momentum distribution, jet mass, jet subjettiness, and jet girth. Additionally, the paper explores the efficacy of this method and its intrinsic limitations. This research marks a significant step towards exploring more efficient simulation methodologies in high-energy particle physics.

Cite As: Ebru Simsek, Bora Isildak, Anil Dogru, Reyhan Aydogan, Burak Bayrak, and Seyda Ertekin, CALPAGAN: Calorimetry for Particles using GANs, Progress of Theoretical and Experimental Physics, 8, 2024

Topic: Generative Adversarial Networks (GANs), Fast Simulations, Calorimeter Data, High-Energy Physics

Type: Journal

Links: https://dl.acm.org/doi/10.1145/3691643

Bibtex: @article{Dogru_ACMTIIS_2024, author= {Dou{g}ru, Ani{}l and Keskin, Mehmet Onur and Aydou{g}an, Reyhan}, title = {Taking into Account Opponent’s Arguments in Human-Agent Negotiations}, year = {2025}, issue_date = {March 2025}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {15}, number = {1}, issn = {2160-6455}, url = {https://doi.org/10.1145/3691643}, doi = {10.1145/3691643}, abstract = {Autonomous negotiating agents, which can interact with other agents, aim to solve decision-making problems involving participants with conflicting interests. Designing agents capable of negotiating with human partners requires considering some factors, such as emotional states and arguments. For this purpose, we introduce an extended taxonomy of argument types capturing human speech acts during the negotiation. We propose an argument-based automated negotiating agent that can extract human arguments from a chat-based environment using a hierarchical classifier. Consequently, the proposed agent can understand the received arguments and adapt its strategy accordingly while negotiating with its human counterparts. We initially conducted human-agent negotiation experiments to construct a negotiation corpus to train our classifier. According to the experimental results, it is seen that the proposed hierarchical classifier successfully extracted the arguments from the given text. Moreover, we conducted a second experiment where we tested the performance of the designed negotiation strategy considering the human opponent’s arguments and emotions. Our results showed that the proposed agent beats the human negotiator and gains higher utility than the baseline agent.}, journal = {ACM Trans. Interact. Intell. Syst.}, month = jan, articleno = {2}, numpages = {35}, keywords = {Human-Agent Negotiation, Argumentation, Opponent Modeling} }

Abstract: Autonomous negotiating agents, which can interact with other agents, aim to solve decision-making problems involving participants with conflicting interests. Designing agents capable of negotiating with human partners requires considering some factors, such as emotional states and arguments. For this purpose, we introduce an extended taxonomy of argument types capturing human speech acts during the negotiation. We propose an argument-based automated negotiating agent that can extract human arguments from a chat-based environment using a hierarchical classifier. Consequently, the proposed agent can understand the received arguments and adapt its strategy accordingly while negotiating with its human counterparts. We initially conducted human-agent negotiation experiments to construct a negotiation corpus to train our classifier. According to the experimental results, it is seen that the proposed hierarchical classifier successfully extracted the arguments from the given text. Moreover, we conducted a second experiment where we tested the performance of the designed negotiation strategy considering the human opponent’s arguments and emotions. Our results showed that the proposed agent beats the human negotiator and gains higher utility than the baseline agent.

Cite As: Anıl Doğru, Mehmet Onur Keskin, Reyhan Aydoğan, “Taking into Account Opponent’s Arguments in Human-Agent Negotiations”, ACM Transactions on Interactive Intelligent Systems,15:1, pp. 1-34, 2025

Topic: Human-Agent Negotiation, Argumentation, Opponent Modeling

Type: Journal

Links: https://link.springer.com/article/10.1007/s10489-023-05001-9

Bibtex: @article{Keskin_AI_2023, title= {Conflict-based negotiation strategy for human-agent negotiation}, volume= {53}, DOI= {10.1007/s10489-023-05001-9}, number= {24}, journal= {Applied Intelligence}, author= {Keskin, Mehmet Onur and Buzcu, Berk and Aydou{g}an, Reyhan}, year= {2023}, pages= {29741--29757} }

Abstract: Day by day, human-agent negotiation becomes more and more vital to reach a socially beneficial agreement when stakeholders need to make a joint decision together. Developing agents who understand not only human preferences but also attitudes is a significant prerequisite for this kind of interaction. Studies on opponent modeling are predominantly based on automated negotiation and may yield good predictions after exchanging hundreds of offers. However, this is not the case in human-agent negotiation in which the total number of rounds does not usually exceed tens. For this reason, an opponent model technique is needed to extract the maximum information gained with limited interaction. This study presents a conflict-based opponent modeling technique and compares its prediction performance with the well-known approaches in human-agent and automated negotiation experimental settings. According to the results of human-agent studies, the proposed model outpr erforms them despite the diversity of participants’ negotiation behaviors. Besides, the conflict-based opponent model estimates the entire bid space much more successfully than its competitors in automated negotiation sessions when a small portion of the outcome space was explored. This study may contribute to developing agents that can perceive their human counterparts’ preferences and behaviors more accurately, acting cooperatively and reaching an admissible settlement for joint interests.

Cite As: Mehmet Onur Keskin, Berk Buzcu, and Reyhan Aydoğan, “Conflict-Based Negotiation Strategy for Human-Agent Negotiation”, Applied Intelligence, Accepted, September 2023.

Topic: Opponent modelling, Preference modelling, Human-agent negotiation, Automated negotiation

Type: Journal

Links: https://www.sciencedirect.com/science/article/pii/S0169260723002018

Bibtex: @article{Magnini_CMPB_2023, title= {Symbolic knowledge extraction for explainable nutritional recommenders}, journal= {Computer Methods and Programs in Biomedicine}, volume= {235}, pages= {107536}, year= {2023}, issn= {0169-2607}, doi= {https://doi.org/10.1016/j.cmpb.2023.107536}, url= {https://www.sciencedirect.com/science/article/pii/S0169260723002018}, author= {Magnini, Matteo and Ciatto, Giovanni and Cant"{u}rk, Furkan and Aydou{g}an, Reyhan and Omicini, Andrea}, keywords= {Explainable artificial intelligence, Symbolic knowledge extraction, Recommendation systems, Nutrition, Neural networks}, abstract= {Background and objective: This paper focuses on nutritional recommendation systems... [abstract truncated]} }

Abstract: Background and objective:This paper focuses on nutritional recommendation systems (RS), i.e. AI-powered automatic systems providing users with suggestions about what to eat to pursue their weight/body shape goals. A trade-off among (potentially) conflictual requirements must be taken into account when designing these kinds of systems, there including: (i) adherence to experts’ prescriptions, (ii) adherence to users’ tastes and preferences, (iii) explainability of the whole recommendation process. Accordingly, in this paper we propose a novel approach to the engineering of nutritional RS, combining machine learning and symbolic knowledge extraction to profile users—hence harmonising the aforementioned requirements. MethodsOur contribution focuses on the data processing workflow. Stemming from neural networks (NN) trained to predict user preferences, we use CART Breiman et al.(1984) to extract symbolic rules in Prolog Körner et al.(2022) form, and we combine them with expert prescriptions brought in similar form. We can then query the resulting symbolic knowledge base via logic solvers, to draw explainable recommendations. ResultsExperiments are performed involving a publicly available dataset of 45,723 recipes, plus 12 synthetic datasets about as many imaginary users, and 6 experts’ prescriptions. Fully-connected 4-layered NN are trained on those datasets, reaching test-set accuracy, on average. Extracted rules, in turn, have fidelity w.r.t. those NN. The resulting recommendation system has a test-set precision of . The symbolic approach makes it possible to devise how the system draws recommendations. ConclusionsThanks to our approach, intelligent agents may learn users’ preferences from data, convert them into symbolic form, and extend them with experts’ goal-directed prescriptions. The resulting recommendations are then simultaneously acceptable for the end user and adequate under a nutritional perspective, while the whole process of recommendation generation is made explainable.

Cite As: Matteo Magnini, Giovanni Citato, Furkan Cantürk, Reyhan Aydoğan and Andrea Omicini, “Symbolic Knowledge Extraction for Explainable Nutritional Recommenders”, Computer Methods and Programs in Biomedicine, 2023.

Topic: Explainable artificial intelligence, Symbolic knowledge extraction, Recommendation systems, Nutrition, Neural networks

Type: Journal

Links: https://ieeexplore.ieee.org/document/10044276

Bibtex: @article{Buzcu_IEEE_2024, title= {Fully autonomous trustworthy unmanned aerial vehicle teamwork: A research guideline using level 2 blockchain}, DOI= {10.1109/mra.2023.3239317}, journal= {IEEE Robotics & Automation Magazine}, author= {Buzcu, Berk and "{O}zg"{u}n, Mert and G{"u}rcan, "{O}nder and Aydou{g}an, Reyhan}, year= {2024}, pages= {2--12} }

Abstract: The vast range of possible fully autonomous multiunmanned aerial vehicle (multi-UAV) operations is creating a new and expanding market where technological advances are happening at a breakneck pace. The integration of UAVs in airspaces (not just for military purposes but also for civil, commercial, and leisure use) is essential in realizing the potential of this growing industry. Furthermore, with the advent of 6G, such integration will be cost-effective and more flexible. However, to reach widespread adoption, new models focusing on the safety, efficiency, reliability, and privacy of fully autonomous multi-UAV operations, ensuring that the operation history is trustworthy and can be audited by the relevant stakeholders, need to be developed. Accordingly, this work presents a research guideline for fully autonomous trustworthy UAV teamwork through layer 2 blockchains that provide efficient, privacy-preserving, reliable, and secure multi-UAV service delivery. We show the implications of this approach for an aerial surveillance use case.

Cite As: Berk Buzcu, Mert Özgün, Önder Güncan, and Reyhan Aydoğan, “Fully Autonomous Trustworthy UAV Teamwork: A Research Guideline Using Level 2 Blockchain”, IEEE Robotics and Automation Magazine, 2023.

Topic: Blockchains, Teamwork, Automation, Reliability, Safety, Privacy, Security

Type: Journal

Links: https://www.mdpi.com/2673-2688/3/3/39

Bibtex: @Article{Turkgeldi_AI_2022, AUTHOR= {T{"u}rkgeldi, Berkay and "{O}zden, Cana Su and Aydou{g}an, Reyhan}, TITLE= {The Effect of Appearance of Virtual Agents in Human-Agent Negotiation}, JOURNAL= {AI}, VOLUME= {3}, YEAR= {2022}, NUMBER= {3}, PAGES= {683--701}, URL= {https://www.mdpi.com/2673-2688/3/3/39}, ISSN= {2673-2688}, ABSTRACT= {Artificial Intelligence (AI) changed our world... [abstract truncated]}, DOI= {10.3390/ai3030039} }

Abstract: Artificial Intelligence (AI) changed our world in various ways. People start to interact with a variety of intelligent systems frequently. As the interaction between human and AI systems increases day by day, the factors influencing their communication have become more and more important, especially in the field of human-agent negotiation. In this study, our aim is to investigate the effect of knowing your negotiation partner (i.e., opponent) with limited knowledge, particularly the effect of familiarity with the opponent during human-agent negotiation so that we can design more effective negotiation systems. As far as we are aware, this is the first study investigating this research question in human-agent negotiation settings. Accordingly, we present a human-agent negotiation framework and conduct a user experiment in which participants negotiate with an avatar whose appearance and voice are a replica of a celebrity of their choice and with an avatar whose appearance and voice are not familiar. The results of the within-subject design experiment show that human participants tend to be more collaborative when their opponent is a celebrity avatar towards whom they have a positive feeling rather than a non-celebrity avatar.

Cite As: Berkay Türkgeldi,Cana Su Özden,Reyhan Aydoğan, “The Effect of Appearance of Virtual Agents in Human-Agent Negotiation“, AI, 3:3, pp 683-701, 2022.

Topic: celebrity vs. non-celebrity, experimental study, familiarity to opponent, human-agent negotiation, interactive intelligent systems, virtual agents

Type: Journal

Links: https://journals.tubitak.gov.tr/cgi/viewcontent.cgi?article=3899&context=elektrik

Bibtex: @article{Arslan_TJEECS_2022, title= {Actor-critic Reinforcement Learning for bidding in bilateral negotiation}, volume= {30}, DOI= {10.55730/1300-0632.3899}, number= {5}, journal= {Turkish Journal of Electrical Engineering and Computer Sciences}, author= {Arslan, Furkan and Aydou{g}an, Reyhan}, year= {2022}, pages= {1695--1714} }

Abstract: Designing an effective and intelligent bidding strategy is one of the most compelling research challenges in automated negotiation, where software agents negotiate with each other to find a mutual agreement when there is a conflict of interests. Instead of designing a hand-crafted decision-making module, this work proposes a novel bidding strategy adopting an actor-critic reinforcement learning approach, which learns what to offer in a bilateral negotiation. An entropy reinforcement learning framework called Soft Actor-Critic (SAC) is applied to the bidding problem, and a self-play approach is employed to train the model. Our model learns to produce the target utility of the coming offer based on previous offer exchanges and remaining time. Furthermore, an imitation learning approach called behavior cloning is adopted to speed up the learning process. Also, a novel reward function is introduced that does take not only the agent?s own utility but also the opponent?s utility at the end of the negotiation. The developed agent is empirically evaluated. Thus, a large number of negotiation sessions are run against a variety of opponents selected in different domains varying in size and opposition. The agent?s performance is compared with its opponents and the performance of the baseline agents negotiating with the same opponents. The empirical results show that our agent successfully negotiates against challenging opponents in different negotiation scenarios without requiring any former information about the opponent or domain in advance. Furthermore, it achieves better results than the baseline agents regarding the received utility at the end of the successful negotiations.

Cite As: Furkan Arslan and Reyhan Aydoğan, “Actor-Critic Reinforcement Learning for Bidding in Bilateral Negotiation”, Turkish Journal of Electrical Engineering & Computer Sciences, 30:5, pp 1695-1714, 2022.

Topic: Deep reinforcement learning, entropy reinforcement learning, imitation learning, automated bilateral negotiation, bidding strategy, multi-agent systems

Type: Journal

Links: https://www.mdpi.com/2072-666X/13/8/1245

Bibtex: @Article{Azarifar_Micromachines_2022, AUTHOR= {Azarifar, Mohammad and Ocaks"{o}nmez, Kerem and Cengiz, Ceren and Aydou{g}an, Reyhan and Arik, Mehmet}, TITLE= {Machine Learning to Predict Junction Temperature Based on Optical Characteristics in Solid-State Lighting Devices: A Test on WLEDs}, JOURNAL= {Micromachines}, VOLUME= {13}, YEAR= {2022}, NUMBER= {8}, ARTICLE-NUMBER= {1245}, URL= {https://www.mdpi.com/2072-666X/13/8/1245}, PubMedID= {36014167}, ISSN= {2072-666X}, ABSTRACT= {While junction temperature control is... [abstract truncated]}, DOI= {10.3390/mi13081245} }

Abstract: While junction temperature control is an indispensable part of having reliable solid-state lighting, there is no direct method to measure its quantity. Among various methods, temperature-sensitive optical parameter-based junction temperature measurement techniques have been used in practice. Researchers calibrate different spectral power distribution behaviors to a specific temperature and then use that to predict the junction temperature. White light in white LEDs is composed of blue chip emission and down-converted emission from photoluminescent particles, each with its own behavior at different temperatures. These two emissions can be combined in an unlimited number of ways to produce diverse white colors at different brightness levels. The shape of the spectral power distribution can, in essence, be compressed into a correlated color temperature (CCT). The intensity level of the spectral power distribution can be inferred from the luminous flux as it is the special weighted integration of the spectral power distribution. This paper demonstrates that knowing the color characteristics and power level provide enough information for possible regressor trainings to predict any white LED junction temperature. A database from manufacturer datasheets is utilized to develop four machine learning-based models, viz., k-Nearest Neighbor (KNN), Radius Near Neighbors (RNN), Random Forest (RF), and Extreme Gradient Booster (XGB). The models were used to predict the junction temperatures from a set of dynamic opto-thermal measurements. This study shows that machine learning algorithms can be employed as reliable novel prediction tools for junction temperature estimation, particularly where measuring equipment limitations exist, as in wafer-level probing or phosphor-coated chips.

Cite As: Mohammad Azarifar, Kerem Ocaksonmez, Ceren Cengiz, Reyhan Aydoğan and Mehmet Arik, “Machine Learning to Predict Junction Temperature Based on Optical Characteristics in Solid-State Lighting Devices: A Test on WLEDs”, Micromachines, 13(8), 2022.

Topic: junction temperature, temperature prediction, light emitting diodes, machine learning, solid-state lighting, gradient boosted trees, random forest

Type: Journal

Links: https://ieeexplore.ieee.org/document/9609976

Bibtex: @ARTICLE{Aydogan_IEEE_2022, author= {Aydou{g}an, Reyhan and Keskin, Onur and c{C}akan, Umut}, journal= {IEEE Transactions on Human-Machine Systems}, title= {Would You Imagine Yourself Negotiating With a Robot, Jennifer? Why Not?}, year= {2022}, volume= {52}, number= {1}, pages= {41--51}, keywords= {Robots;Humanoid robots;Mood;Protocols;Task analysis;Resource management;Man-machine systems;Effect of gestures in negotiation;human--agent negotiation;human--robot negotiation}, doi= {10.1109/THMS.2021.3121664} }

Abstract: With the improvement of intelligent systems and robotics, social robots are becoming part of our society. To accomplish complex tasks, robots and humans may need to collaborate, and when necessary, they need to negotiate with each other. While designing such socially interacting robots, it is crucial to consider human factors such as facial expression, emotions, and body language. Since gestures play a crucial role in interaction, this article studies the effect of gestures in human–robot negotiation experiments. Additionally, it compares the performance of variants of the well-known negotiation tactics (i.e., time-based and behavior-based) in automated negotiation literature in the context of human–robot negotiations. Our experimental results support the finding in automated negotiation. That is, the robot gained higher utility when it imitates its opponent’s bidding strategy than employing a time-based negotiation strategy. When adopting a behavior-based technique, there is a statistically significant effect of gestures on the underlying negotiation process, and, therefore, on negotiation outcome.

Cite As: Reyhan Aydoğan, Onur Keskin and Umut Çakan, “Would you imagine yourself negotiating with a robot, Jennifer? Why not?”, IEEE Transactions on Human-Machine Systems, Accepted, October 2021.

Topic: Effect of gestures in negotiation, human–agent negotiation, human–robot negotiation

Type: Journal

Links: https://dl.acm.org/doi/10.1145/3465054

Bibtex: @article{Aydogan_ACMTIST_2021, author= {Aydou{g}an, Reyhan and Kafali, "{O}zg"{u}r and Arslan, Furkan and Jonker, Catholijn M. and Singh, Munindar P.}, title= {Nova: Value-based Negotiation of Norms}, year= {2021}, issue_date= {August 2021}, publisher= {Association for Computing Machinery}, address= {New York, NY, USA}, volume= {12}, number= {4}, issn= {2157-6904}, url= {https://doi.org/10.1145/3465054}, doi= {10.1145/3465054}, abstract= {Specifying a normative multiagent system (nMAS)... [abstract truncated]}, journal= {ACM Trans. Intell. Syst. Technol.}, articleno= {45}, numpages= {29}, keywords= {human--agent negotiation, conflicting requirements, Sociotechnical systems} }

Abstract: Specifying a normative multiagent system (nMAS) is challenging, because different agents often have conflicting requirements. Whereas existing approaches can resolve clear-cut conflicts, tradeoffs might occur in practice among alternative nMAS specifications with no apparent resolution. To produce an nMAS specification that is acceptable to each agent, we model the specification process as a negotiation over a set of norms. We propose an agent-based negotiation framework, where agents’ requirements are represented as values (e.g., patient safety, privacy, and national security), and an agent revises the nMAS specification to promote its values by executing a set of norm revision rules that incorporate ontology-based reasoning. To demonstrate that our framework supports creating a transparent and accountable nMAS specification, we conduct an experiment with human participants who negotiate against our agent. Our findings show that our negotiation agent reaches better agreements (with small p-value and large effect size) faster than a baseline strategy. Moreover, participants perceive that our agent enables more collaborative and transparent negotiations than the baseline (with small p-value and large effect size in particular settings) toward reaching an agreement.

Cite As: Reyhan Aydoğan, Özgür Kafalı, Furkan Arslan, Catholijn M. Jonker, and Munindar P. Singh, “Nova: Value-Based Negotiation of Norms”, ACM Transactions on Intelligent Systems and Technology , 12:4, pp 1–29, 2021.

Topic: Normative Multiagent Systems, Nova

Type: Journal

Links: https://www.sciencedirect.com/science/article/abs/pii/S1567422321000302

Bibtex: @article{Ayvaz_ECRA_2021, title= {Campaign participation prediction with deep learning}, journal= {Electronic Commerce Research and Applications}, volume= {48}, pages= {101058}, year= {2021}, issn= {1567-4223}, doi= {https://doi.org/10.1016/j.elerap.2021.101058}, url= {https://www.sciencedirect.com/science/article/pii/S1567422321000302}, author= {Ayvaz, Demet and Aydou{g}an, Reyhan and Akc{c}ura, M. Tolga and c{S}ensoy, Murat}, keywords= {Deep learning, Decision tree classification, & network models, Feature extraction, Real-time marketing}, abstract= {Increasingly, on-demand nature of customer interactions... [abstract truncated]} }

Abstract: Increasingly, on-demand nature of customer interactions put pressure on companies to build real-time campaign management systems. Instead of having managers to decide on the campaign rules, such as, when, how and whom to offer, creating intelligent campaign management systems that can automate such decisions is essential. In addition, regulations or company policies usually restrict the number of accesses to the customers. Efficient learning of customer behaviour through dynamic campaign participation observations becomes a crucial feature that may ultimately define customer satisfaction and retention. This paper builds on the recent successes of deep learning techniques and proposes a classification model to predict customer responses for campaigns. Classic deep neural networks are good at learning hidden relations within data (i.e., patterns) but with limited capability for memorization. One solution to increase memorization is to use manually craft features, as in Wide & Deep networks, which are originally proposed for Google Play App. recommendations. We advocate using decision trees as an easier way of mining high-level relationships for enhancing Wide & Deep networks. Such an approach has the added benefit of beating manually created rules, which, most of the time, use incomplete data and have biases. A set of comprehensive experiments on campaign participation data from a leading GSM provider shows that automatically crafted features make a significant increase in the accuracy and outperform Deep and Wide & Deep models with manually crafted features.

Cite As: Demet Ayvaz, Reyhan Aydoğan, M. Tolga Akçura, and Murat Şensoy, “Campaign Participation Prediction with Deep Learning”, Electronic Commerce Research and Applications, 48:1, 2021.

Topic: Deep learning, Decision tree classification, Wide & Deep network models, Feature extraction, Real-time marketing

Type: Journal

Links: https://www.mdpi.com/2076-3417/11/13/6022

Bibtex: @Article{Victor_AS_2021, AUTHOR= {Sanchez-Anguix, Victor and Tunali{}, Okan and Aydou{g}an, Reyhan and Julian, Vicente}, TITLE= {Can Social Agents Efficiently Perform in Automated Negotiation?}, JOURNAL= {Applied Sciences}, VOLUME= {11}, YEAR= {2021}, NUMBER= {13}, ARTICLE-NUMBER= {6022}, URL= {https://www.mdpi.com/2076-3417/11/13/6022}, ISSN= {2076-3417}, ABSTRACT= {In the last few years, we witnessed... [abstract truncated]}, DOI= {10.3390/app11136022} }

Abstract: In the last few years, we witnessed a growing body of literature about automated negotiation. Mainly, negotiating agents are either purely self-driven by maximizing their utility function or by assuming a cooperative stance by all parties involved in the negotiation. We argue that, while optimizing one’s utility function is essential, agents in a society should not ignore the opponent’s utility in the final agreement to improve the agent’s long-term perspectives in the system. This article aims to show whether it is possible to design a social agent (i.e., one that aims to optimize both sides’ utility functions) while performing efficiently in an agent society. Accordingly, we propose a social agent supported by a portfolio of strategies, a novel tit-for-tat concession mechanism, and a frequency-based opponent modeling mechanism capable of adapting its behavior according to the opponent’s behavior and the state of the negotiation. The results show that the proposed social agent not only maximizes social metrics such as the distance to the Nash bargaining point or the Kalai point but also is shown to be a pure and mixed equilibrium strategy in some realistic agent societies.

Cite As: Victor Sanchez-Anguix, Okan Tunalı, Reyhan Aydoğan and Vincente Julian, “Can social agents efficiently perform in automated negotiation? ”, Applied Science, 11 (13): 6022, 2021.

Topic: automated negotiation, intelligent agents, multiagent systems, agreement technologies, heuristic negotiation, optimization

Type: Journal

Links: https://urldefense.proofpoint.com/v2/url?u=https-3A__www.mdpi.com_2226-2D4310_7_12_171_pdf&d=DwIDaQ&c=XYzUhXBD2cD-CornpT4QE19xOJBbRy-TBPLK0X9U2o8&r=DNa17Dgos13LMqSO4BM14OZ9cQxzs39EIokLhVaK5zc&m=4A8pTipNKHNY9XifKnLtnLX4afMrVp7qDdH2qSmdAvQ&s=Vf5_YgmbZlaLqs2HU2MPBnOlT-jom5m5xxZPUGkxbNo&e=

Bibtex: @Article{Dogru_Aerospace_2020, AUTHOR= {Dou{g}ru, Ani{}l and Bouarfa, Soufiane and Arizar, Ridwan and Aydou{g}an, Reyhan}, TITLE= {Using Convolutional Neural Networks to Automate Aircraft Maintenance Visual Inspection}, JOURNAL= {Aerospace}, VOLUME= {7}, YEAR= {2020}, NUMBER= {12}, ARTICLE-NUMBER= {171}, URL= {https://www.mdpi.com/2226-4310/7/12/171}, ISSN= {2226-4310}, ABSTRACT= {Convolutional Neural Networks combined with autonomous drones... [abstract truncated]}, DOI= {10.3390/aerospace7120171} }

Abstract: Convolutional Neural Networks combined with autonomous drones are increasingly seen as enablers of partially automating the aircraft maintenance visual inspection process. Such an innovative concept can have a significant impact on aircraft operations. Though supporting aircraft maintenance engineers detect and classify a wide range of defects, the time spent on inspection can significantly be reduced. Examples of defects that can be automatically detected include aircraft dents, paint defects, cracks and holes, and lightning strike damage. Additionally, this concept could also increase the accuracy of damage detection and reduce the number of aircraft inspection incidents related to human factors like fatigue and time pressure. In our previous work, we have applied a recent Convolutional Neural Network architecture known by MASK R-CNN to detect aircraft dents. MASK-RCNN was chosen because it enables the detection of multiple objects in an image while simultaneously generating a segmentation mask for each instance. The previously obtained F1 and F2 scores were 62.67% and 59.35%, respectively. This paper extends the previous work by applying different techniques to improve and evaluate prediction performance experimentally. The approach uses include (1) Balancing the original dataset by adding images without dents; (2) Increasing data homogeneity by focusing on wing images only; (3) Exploring the potential of three augmentation techniques in improving model performance namely flipping, rotating, and blurring; and (4) using a pre-classifier in combination with MASK R-CNN. The results show that a hybrid approach combining MASK R-CNN and augmentation techniques leads to an improved performance with an F1 score of (67.50%) and F2 score of (66.37%).

Cite As: Anıl Doğru, Soufiane Bouarfa, Ridwan Arizar, Reyhan Aydoğan, “Using Convolutional Neural Networks to Automate Aircraft Maintenance Visual Inspection”, Aerospace, Application of Multiagent Systems and Artificial Intelligence Techniques in Aviation (Volume II ), 2020.

Topic: aircraft maintenance inspection, anomaly detection, defect inspection, convolutional neural networks, Mask R-CNN, generative adversarial networks, image augmentation

Type: Journal

Links: https://link.springer.com/article/10.1007/s40860-020-00123-0

Bibtex: @article{Bouarfa_RIE_2021, title= {Formal modelling and verification of a multi-agent negotiation approach for airline Operations Control}, volume= {7}, DOI= {10.1007/s40860-020-00123-0}, number= {4}, journal= {Journal of Reliable Intelligent Environments}, author= {Bouarfa, Soufiane and Aydou{g}an, Reyhan and Sharpanskykh, Alexei}, year= {2021}, pages= {279--298} }

Abstract: This paper proposes and evaluates a new airline disruption management strategy using multi-agent system modelling, simulation, and verification. This new strategy is based on a multi-agent negotiation protocol and is compared with three airline strategies based on established industry practices. The application concerns Airline Operations Control whose core functionality is disruption management. To evaluate the new strategy, a rule-based multi-agent system model of the AOC and crew processes has been developed. This model is used to assess the effects of multi-agent negotiation on airline performance in the context of a challenging disruption scenario. For the specific scenario considered, the multi-agent negotiation strategy outperforms the established strategies when the agents involved in the negotiation are experts. Another important contribution is that the paper presents a logic-based ontology used for formal modelling and analysis of AOC workflows.

Cite As: Soufiane Bouarfa, Reyhan Aydoğan, and Alexei Sharpanskykh, “Formal Modelling and Verification of a Multi-Agent Negotiation Approach for Airline Operations Control”, Journal of Reliable Intelligent Environments, Accepted, November 2020.

Topic: Workflow modelling, Rule-based modelling, Formal modelling, Multi-agent negotiation, Model checking, Airline operations control

Type: Journal

Links: https://journals.tubitak.gov.tr/elektrik/vol28/iss4/2/

Bibtex: @article{RAZEGHI_TJEECS_2020, title= {Deep reinforcement learning for acceptance strategy in bilateral negotiations}, volume= {28}, DOI= {10.3906/elk-1907-215}, number= {4}, journal= {TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES}, author= {Razegi, Yousef and Yavuz, Ozan and Aydou{g}an, Reyhan}, year= {2020}, pages= {1824--1840} }

Abstract: This paper introduces an acceptance strategy based on reinforcement learning for automated bilateral negotiation, where negotiating agents bargain on multiple issues in a variety of negotiation scenarios. Several acceptance strategies based on predefined rules have been introduced in the automated negotiation literature. Those rules mostly rely on some heuristics, which take time and/or utility into account. For some negotiation settings, an acceptance strategy solely based on a negotiation deadline might perform well; however, it might fail in another setting. Instead of following predefined acceptance rules, this paper presents an acceptance strategy that aims to learn whether to accept its opponent's offer or make a counter offer by reinforcement signals received after performing an action. In an experimental setup, it is shown that the performance of the proposed approach improves over time.

Cite As: Yousef Razeghi, Ozan Yavuz, and Reyhan Aydoğan, “Deep Reinforcement Learning for Acceptance Strategy in Bilateral Negotiations”, Turkish Journal of Electrical Engineering & Computer Sciences, 28:1, pp. 1824-2840, 2020.

Topic: Deep reinforcement learning, automated bilateral negotiation, acceptance strategy

Type: Journal

Links: https://www.sciencedirect.com/science/article/abs/pii/S0167739X19305795

Bibtex: @article{Gungor_FGCS_2019, title= {Algorithm selection and combining multiple learners for residential energy prediction}, journal= {Future Generation Computer Systems}, volume= {99}, pages= {391--400}, year= {2019}, issn= {0167-739X}, doi= {https://doi.org/10.1016/j.future.2019.04.018}, url= {https://www.sciencedirect.com/science/article/pii/S0167739X19305795}, author= {G{"u}ng{"o}r, Onat and Akc{s}anli, Bari{}c{s} and Aydou{g}an, Reyhan}, keywords= {Electricity consumption prediction, Algorithm selection, Combining multiple learners, Time series prediction}, abstract= {Balancing supply and demand management in energy grids... [abstract truncated]} }

Abstract: Balancing supply and demand management in energy grids requires knowing energy consumption in advance. Therefore, forecasting residential energy consumption accurately plays a key role for future energy systems. For this purpose, in the literature a number of prediction algorithms have been used. This work aims to increase the accuracy of those predictions as much as possible. Accordingly, we first introduce an algorithm selection approach, which identifies the best prediction algorithm for the given residence with respect to its characteristics such as number of people living, appliances and so on. In addition to this, we also study combining multiple learners to increase the accuracy of the predictions. In our experimental setup, we evaluate the aforementioned approaches. Empirical results show that adopting an algorithm selection approach performs better than any single prediction algorithm. Furthermore, combining multiple learners increases the accuracy of the energy consumption prediction significantly.

Cite As: Onat Güngör, Barış Akşanlı and Reyhan Aydoğan, “Algorithm Selection and Combining Multiple Learners for Residential Energy Prediction”, Future Generation Computer Systems (FGCS), Vol: 99, pp. 391-400, 2019.

Topic: Electricity consumption prediction, Algorithm selection, Combining multiple learners, Time series prediction

Type: Journal

Links: https://www.sciencedirect.com/science/article/abs/pii/S1568494618306811

Bibtex: @article{Sanchez_ASC_2019, title= {A near Pareto optimal approach to student--supervisor allocation with two sided preferences and workload balance}, journal= {Applied Soft Computing}, volume= {76}, pages= {1--15}, year= {2019}, issn= {1568-4946}, doi= {https://doi.org/10.1016/j.asoc.2018.11.049}, url= {https://www.sciencedirect.com/science/article/pii/S1568494618306811}, author= {Sanchez-Anguix, Victor and Chalumuri, Rithin and Aydou{g}an, Reyhan and Julian, Vicente}, keywords= {Genetic algorithms, student--project allocation, Matching, Pareto optimal, Artificial intelligence}, abstract= {The problem of allocating students to supervisors... [abstract truncated]} }

Abstract: The problem of allocating students to supervisors for the development of a personal project or a dissertation is a crucial activity in the higher education environment, as it enables students to get feedback on their work from an expert and improve their personal, academic, and professional abilities. In this article, we propose a multi-objective and near Pareto optimal genetic algorithm for the allocation of students to supervisors. The allocation takes into consideration the students and supervisors’ preferences on research/project topics, the lower and upper supervision quotas of supervisors, as well as the workload balance amongst supervisors. We introduce novel mutation and crossover operators for the student–supervisor allocation problem. The experiments carried out show that the components of the genetic algorithm are more apt for the problem than classic components, and that the genetic algorithm is capable of producing allocations that are near Pareto optimal in a reasonable time.

Cite As: Victor Sanchez-Anguix, Rithin Chalumuri, Reyhan Aydoğan, and Vicente Julian, “A near Pareto optimal approach to student-supervisor allocation with two sided preferences and workload balance”, Applied Soft Computing, Vol:76, pp 1-15, 2019.

Topic: Genetic algorithms, student–project allocation, Matching, Pareto optimal, Artificial intelligence

Type: Journal

Links: https://link.springer.com/article/10.1007/s10115-018-01325-y

Bibtex: @article{Sanchez_KIS_2019, title= {Bottom-up approaches to achieve pareto optimal agreements in group decision making}, volume= {61}, DOI= {10.1007/s10115-018-01325-y}, number= {2}, journal= {Knowledge and Information Systems}, author= {Sanchez-Anguix, Victor and Aydou{g}an, Reyhan and Baarslag, Tim and Jonker, Catholijn}, year= {2019}, pages= {1019--1046} }

Abstract: In this article, we introduce a new paradigm to achieve Pareto optimality in group decision-making processes: bottom-up approaches to Pareto optimality. It is based on the idea that, while resolving a conflict in a group, individuals may trust some members more than others; thus, they may be willing to cooperate and share more information with those members. Therefore, one can divide the group into subgroups where more cooperative mechanisms can be formed to reach Pareto optimal outcomes. This is the first work that studies such use of a bottom-up approach to achieve Pareto optimality in conflict resolution in groups. First, we prove that an outcome that is Pareto optimal for subgroups is also Pareto optimal for the group as a whole. Then, we empirically analyze the appropriate conditions and achievable performance when applying bottom-up approaches under a wide variety of scenarios based on real-life datasets. The results show that bottom-up approaches are a viable mechanism to achieve Pareto optimality with applications to group decision-making, negotiation teams, and decision making in open environments.

Cite As: Victor Sanchez-Anguix, Reyhan Aydoğan, Tim Baarlag, and Catholijn M. Jonker, “Bottom-up approaches to achieve Pareto optimal agreements in group decision making”, Knowledge and Information System (KAIS), 61: 1, pp. 1019-1046, 2019.

Topic: Agreement technologies, Automated negotiation, Pareto optimality, Group decision making, Multi-agent systems

Type: Journal

Links: https://link.springer.com/article/10.1007/s11518-018-5369-5

Bibtex: @Article{Aydogan_SSSE_2018, author= {Aydou{g}an, Reyhan and Marsa-Maestre, Ivan and Klein, Mark and Jonker, Catholijn M.}, title= {A Machine Learning Approach for Mechanism Selection in Complex Negotiations}, journal= {Journal of Systems Science and Systems Engineering}, year= {2018}, volume= {27}, number= {2}, pages= {134--155}, abstract= {Automated negotiation mechanisms can be helpful... [abstract truncated]}, issn= {1861-9576}, doi= {10.1007/s11518-018-5369-5}, url= {https://doi.org/10.1007/s11518-018-5369-5} }

Abstract: Automated negotiation mechanisms can be helpful in contexts where users want to reach mutually satisfactory agreements about issues of shared interest, especially for complex problems with many interdependent issues. A variety of automated negotiation mechanisms have been proposed in the literature. The effectiveness of those mechanisms, however, may depend on the characteristics of the underlying negotiation problem (e.g. on the complexity of participant's utility functions, as well as the degree of conflict between participants). While one mechanism may be a good choice for a negotiation problem, it may be a poor choice for another. In this paper, we pursue the problem of selecting the most effective negotiation mechanism given a particular problem by (1) defining a set of scenario metrics to capture the relevant features of negotiation problems, (2) evaluating the performance of a range of negotiation mechanisms on a diverse test suite of negotiation scenarios, (3) applying machine learning techniques to identify which mechanisms work best with which scenarios, and (4) demonstrating that using these classification rules for mechanism selection enables significantly better negotiation performance than any single mechanism alone.

Cite As: Reyhan Aydoğan, Ivan Marsa-Maestre, Mark Klein, and Catholijn M. Jonker, “A Machine Learning Approach for Mechanism Selection in Complex Negotiations”, Special Issue on Agent-Based Modelling for Complex Systems, Journal of Systems Science and Systems Engineering, Volume 27, Issue 2, pp 134–155, 2018.

Topic: Automated negotiation, mechanism selection, scenario metrics

Type: Journal

Links: https://doi.org/10.1609/aimag.v36i4.2609

Bibtex: @article{Baarslag_ANAC_2015, author= {Baarslag, Tim and Aydou{g}an, Reyhan and Hindriks, Koen V. and Fujita, Katsuhide and Ito, Takayuki and Jonker, Catholijn M.}, title= {The Automated Negotiating Agents Competition, 2010--2015}, journal= {AI Magazine}, volume= {36}, number= {4}, pages= {115--118}, doi= {https://doi.org/10.1609/aimag.v36i4.2609}, url= {https://onlinelibrary.wiley.com/doi/abs/10.1609/aimag.v36i4.2609}, eprint= {https://onlinelibrary.wiley.com/doi/pdf/10.1609/aimag.v36i4.2609}, abstract= {The Automated Negotiating Agents Competition is an international event... [abstract truncated]}, year= {2015} }

Abstract: The Automated Negotiating Agents Competition is an international event that, since 2010, has contributed to the evaluation and development of new techniques and benchmarks for improving the state-of-the-art in automated multi-issue negotiation. A key objective of the competition has been to analyze and search the design space of negotiating agents for agents that are able to operate effectively across a variety of domains. The competition is a valuable tool for studying important aspects of negotiation including profiles and domains, opponent learning, strategies, bilateral and multilateral protocols. Two of the challenges that remain are: How to develop argumentation-based negotiation agents that next to bids, can inform and argue to obtain an acceptable agreement for both parties, and how to create agents that can negotiate in a human fashion.

Cite As: Tim Baarslag, Reyhan Aydoğan, Koen Hindriks, Catholijn M. Jonker, Katsuhide Fujita, and Takayuki Ito, “The Automated Negotiating Agents Competition, 2010-2015”, AI Magazine, vol. 36. pp. 115-118, 2015.

Type: Journal

Links: https://link.springer.com/article/10.1007/s10115-014-0798-z

Bibtex: @Article{Aydogan_KIS_2015, author= {Aydou{g}an, Reyhan and Baarslag, Tim and Hindriks, Koen V. and Jonker, Catholijn M. and Yolum, P{i}nar}, title= {Heuristics for using CP-nets in utility-based negotiation without knowing utilities}, journal= {Knowledge and Information Systems}, year= {2015}, day= {01}, volume= {45}, number= {2}, pages= {357--388}, abstract= {CP-nets have proven to be an effective representation... [abstract truncated]}, issn= {0219-3116}, doi= {10.1007/s10115-014-0798-z}, url= {https://doi.org/10.1007/s10115-014-0798-z} }

Abstract: CP-nets have proven to be an effective representation for capturing preferences. However, their use in automated negotiation is not straightforward because, typically, preferences in CP-nets are partially ordered and negotiating agents are required to compare any two outcomes based on a request and an offer in order to negotiate effectively. If agents know how to generate total orders from their CP-nets, they can make this comparison. This paper proposes heuristics that enable the use of CP-nets in utility-based negotiations by generating total orderings. To validate this approach, the paper compares the performance of CP-nets with our heuristics with the performance of UCP-nets that are equipped with complete preference orderings. Our results show that we can achieve comparable performance in terms of the outcome utility. More importantly, one of our proposed heuristics can achieve this performance with significantly smaller number of interactions compared to UCP-nets.

Cite As: Reyhan Aydoğan, Tim Baarslag, Koen Hindriks, Catholijn M. Jonker, and Pınar Yolum, “Heuristics for using CP-nets in utility-based negotiation without knowing utilities”, Knowledge and Information System (KAIS), published online, pp. 1-32, 2014.

Topic: Automated negotiation, Qualitative preferences, CP-nets, Heuristic-based approaches

Type: Journal

Links: https://www.sciencedirect.com/science/article/abs/pii/S1567422314000283

Bibtex: @article{Sanchez_ECRA_2014, title= {Unanimously acceptable agreements for negotiation teams in unpredictable domains}, journal= {Electronic Commerce Research and Applications}, volume= {13}, number= {4}, pages= {243--265}, year= {2014}, issn= {1567-4223}, doi= {https://doi.org/10.1016/j.elerap.2014.05.002}, url= {https://www.sciencedirect.com/science/article/pii/S1567422314000283}, author= {Sanchez-Anguix, Victor and Aydou{g}an, Reyhan and Julian, Vicente and Jonker, Catholijn}, keywords= {Automated negotiation, Multi-agent systems, Agreement technologies}, abstract= {A negotiation team is a set of agents... [abstract truncated]} }

Abstract: A negotiation team is a set of agents with common and possibly also conflicting preferences that forms one of the parties of a negotiation. A negotiation team is involved in two decision making processes simultaneously, a negotiation with the opponents, and an intra-team process to decide on the moves to make in the negotiation. This article focuses on negotiation team decision making for circumstances that require unanimity of team decisions. Existing agent-based approaches only guarantee unanimity in teams negotiating in domains exclusively composed of predictable and compatible issues. This article presents a model for negotiation teams that guarantees unanimous team decisions in domains consisting of predictable and compatible, and alsounpredictable issues. Moreover, the article explores the influence of using opponent, and team member models in the proposing strategies that team members use. Experimental results show that the team benefits if team members employ Bayesian learning to model their teammates’ preferences.

Cite As: Victor Sanchez-Anguix, Reyhan Aydoğan, Vicente Julian, Ana Garcia-Fornes and Catholijn Jonker, “Unanimously Acceptable Agreements for Negotiation Teams in Unpredictable Domains”, Electronic Commerce and Applications, 13:4, pp 243-265, 2014.

Topic: Automated negotiation, Multi-agent systems, Agreement technologies

Type: Journal

Links: http://www.sciencedirect.com/science/article/pii/S016792361300167X

Bibtex: @article{Marsa_DSS_2014, title= {From problems to protocols: Towards a negotiation handbook}, journal= {Decision Support Systems}, volume= {60}, pages= {39--54}, year= {2014}, note= {Automated Negotiation Technologies and their Applications}, issn= {0167-9236}, doi= {https://doi.org/10.1016/j.dss.2013.05.019}, url= {https://www.sciencedirect.com/science/article/pii/S016792361300167X}, author= {Marsa-Maestre, Ivan and Klein, Mark and Jonker, Catholijn M. and Aydou{g}an, Reyhan}, keywords= {Automated negotiation, Scenario metrics, Scenario generation, Testbed framework, Negotiation repository}, abstract= {Automated negotiation protocols represent... [abstract truncated]} }

Abstract: Automated negotiation protocols represent a potentially powerful tool for problem solving in decision support systems involving participants with conflicting interests. However, the effectiveness of negotiation approaches depends greatly on the negotiation problem under consideration. Since there is no one negotiation protocol that clearly outperforms all others in all scenarios, we need to be able to decide which protocol is most suited for each particular problem. The goal of our work is to meet this challenge by defining a “negotiation handbook”, that is, a collection of design rules which allow us, given a particular negotiation problem, to choose the most appropriate protocol to address it. This paper describes our progress towards this goal, including a tool for generating a wide range of negotiation scenarios, a set of high-level metrics for characterizing how negotiation scenarios differ, a testbed environment for evaluating protocol performance with different scenarios, and a community repository which allows us to systematically record and analyze protocol performance data.

Cite As: Ivan Marsa-Maestrea, Mark Klein, Catholijn M. Jonker, and Reyhan Aydoğan, “From Problems to Protocols: Towards a Negotiation Handbook”, Decision Support Systems and Electronic Commerce, Vol. 60, p.p. 39-54, 2014.

Topic: Ivan Marsa-Maestre and Mark Klein and Catholijn M. Jonker and Reyhan Aydoğan

Type: Journal

Links: http://link.springer.com/article/10.1007%2Fs10458-010-9147-0

Bibtex: @Article{Aydogan_AAMAS_2012, author= {Aydou{g}an, Reyhan and Yolum, P{i}nar}, journal= {Autonomous Agents and Multi-Agent Systems}, title= {Learning opponent's preferences for effective negotiation: an approach based on concept learning}, year= {2012}, volume= {24}, number= {1}, pages= {104--140}, abstract= {We consider automated negotiation as a process... [abstract truncated]}, issn= {1573-7454}, doi= {10.1007/s10458-010-9147-0}, url= {https://doi.org/10.1007/s10458-010-9147-0} }

Abstract: We consider automated negotiation as a process carried out by software agents to reach a consensus. To automate negotiation, we expect agents to understand their user’s preferences, generate offers that will satisfy their user, and decide whether counter offers are satisfactory. For this purpose, a crucial aspect is the treatment of preferences. An agent not only needs to understand its own user’s preferences, but also its opponent’s preferences so that agreements can be reached. Accordingly, this paper proposes a learning algorithm that can be used by a producer during negotiation to understand consumer’s needs and to offer services that respect consumer’s preferences. Our proposed algorithm is based on inductive learning but also incorporates the idea of revision. Thus, as the negotiation proceeds, a producer can revise its idea of the consumer’s preferences. The learning is enhanced with the use of ontologies so that similar service requests can be identified and treated similarly. Further, the algorithm is targeted to learning both conjunctive as well as disjunctive preferences. Hence, even if the consumer’s preferences are specified in complex ways, our algorithm can learn and guide the producer to create well-targeted offers. Further, our algorithm can detect whether some preferences cannot be satisfied early and thus consensus cannot be reached. Our experimental results show that the producer using our learning algorithm negotiates faster and more successfully with customers compared to several other algorithms.

Cite As: Reyhan Aydoğan and Pınar Yolum, “Learning Opponents Preferences for Effective Negotiation: An Approach Based on Concept Learning”. Journal of Autonomous Agents and Multi-Agent Systems, 24:1, pp. 104-140, 2012.

Topic: Negotiation, Preference Learning, Ontology Reasoning, Disjunctive Preferences

Type: Journal

Links: https://dl.acm.org/doi/10.1145/3610978.3640759

Bibtex: @inproceedings{Keskin_HRI_2024, author = {Keskin, M. Onur and Akay, Selen and Dou{g}an, Ayc{s}e and Koc{c}yigit, Berkecan and Kanero, Junko and Aydou{g}an, Reyhan}, title = {You Look Nice, but I Am Here to Negotiate: The Influence of Robot Appearance on Negotiation Dynamics}, year = {2024}, isbn = {9798400703232}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3610978.3640759}, doi = {10.1145/3610978.3640759}, abstract = {This report presents two experimental studies examining whether relatively subtle differences in the appearances of humanoid robots impact (1) the outcomes of human-robot negotiation (i.e., utility scores) and (2) the participant's attitudes toward their robot negotiation partner. Study I compared Nao and Pepper, and Study II compared Nao and QT in identical negotiation settings. While the appearance of robots influenced the participant's attitudes toward the robot before and after the negotiation, such differences were not manifested in the utility scores. The consistent utility scores across different robots reassure that minor variations in the visual characteristics of robots do not alter how users negotiate with a robot. Yet, as participants felt differently about the three robots, there remains the possibility that the differences in their appearances may influence the user's initial inclination to approach each robot. As among the first to systematically investigate the influence of robot appearance on human-robot negotiations, this study emphasizes the importance of assessing both objective outcome scores and the subjective experience of the user in human-robot interaction (HRI) research and offers valuable insights for designing and implementing social robots in real-world settings including customer service and other AI-based interactions.}, booktitle = {Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction}, pages = {598--602}, numpages = {5}, keywords = {appearance, human-robot negotiation, humanoid robots}, location = {Boulder, CO, USA}, series = {HRI '24} }

Abstract: This report presents two experimental studies examining whether relatively subtle differences in the appearances of humanoid robots impact (1) the outcomes of human-robot negotiation (i.e., utility scores) and (2) the participant's attitudes toward their robot negotiation partner. Study I compared Nao and Pepper, and Study II compared Nao and QT in identical negotiation settings. While the appearance of robots influenced the participant's attitudes toward the robot before and after the negotiation, such differences were not manifested in the utility scores. The consistent utility scores across different robots reassure that minor variations in the visual characteristics of robots do not alter how users negotiate with a robot. Yet, as participants felt differently about the three robots, there remains the possibility that the differences in their appearances may influence the user's initial inclination to approach each robot. As among the first to systematically investigate the influence of robot appearance on human-robot negotiations, this study emphasizes the importance of assessing both objective outcome scores and the subjective experience of the user in human-robot interaction (HRI) research and offers valuable insights for designing and implementing social robots in real-world settings including customer service and other AI-based interactions.

Cite As: Mehmet Onur Keskin, Selen Akay, Ayse Doğan, Berkecan Koçyiğit, Junko Kanero, and Reyhan Aydoğan, “You Look Nice, but I Am Here to Negotiate: The Influence of Robot Appearance on Negotiation Dynamics“, ACM/IEEE Conference on Human Robot Interaction (HRI-2024), Late Breaking Reports, Colorado, USA, 2024

Type: Conference

Links: https://ec-3.org/publications/conferences/EC32024/papers/EC32024_283.pdf

Bibtex: @inproceedings{Bolluk_EC3_2024, doi = {10.35490/EC3.2024.283}, url = {https://ec-3.org/publications/conference/paper/?id=EC32024_283}, year = {2024}, month = {July}, publisher = {European Council on Computing in Construction}, author = {Bolluk, Muhammed Said and Seyis, Senem and Aydou{g}an, Reyhan and Kalayc{i}ou{g}lu "{O}zdemir, Ece}, title = {A Simplified Bayesian Approach for The Calibration of District-Building Energy Models}, booktitle = {Proceedings of the 2024 European Conference on Computing in Construction}, volume = {5}, isbn = {978-9-083451-30-5}, address = {Chania, Greece}, series = {Computing in Construction}, language = {en-GB}, abstract = {Bayesian optimization with surrogate modeling is widely used to calibrate building energy models. However, complexities arise in surrogate modeling due to the variability in building morphology at the urban scale. Thus, maintaining dynamic simulation accuracy is crucial. This study presents a novel optimization framework for calibrating district-building energy models using Bayesian decision theory. Once tested on a case study district, the approach reduces monthly calibration error by approximately 45%. Future works could be employing more robust classifiers and handling imbalanced target variables. The proposed approach can minimize computational demands for optimizing dynamic models while ensuring reliability.}, issn = {2684-1150}, Organisation = {European Conference on Computing in Construction}, Editors = {Marijana Sre{'c}kovi{'c}, Mohamad Kassem, Ranjith Soman, Athanasios Chassiakos } }

Abstract: Bayesian optimization with surrogate modeling is widely used to calibrate building energy models. However, complexities arise in surrogate modeling due to the variability in building morphology at the urban scale. Thus, maintaining dynamic simulation accuracy is crucial. This study presents a novel optimization framework for calibrating district-building energy models using Bayesian decision theory. Once tested on a case study district, the approach reduces monthly calibration error by approximately 45%. Future works could be employing more robust classifiers and handling imbalanced target variables. The proposed approach can minimize computational demands for optimizing dynamic models while ensuring reliability

Cite As: Said Bolluk, Senem Seyis, Reyhan Aydoğan, and Ece Kalaycıoğlu Özdemir, “A Simplified Bayesian Approach for the Calibration of District-Building Energy Models”, 2024 European Conference on Computing in Construction, Crete, Greece July 14-17, 2024

Type: Conference

Links: https://dl.acm.org/doi/abs/10.5555/3635637.3663098

Bibtex: @inproceedings{Buzcu_AAMAS_2024, author = {Buzcu, Berk and Kuru, Emre and Aydou{g}an, Reyhan}, title = {User-centric Explanation Strategies for Interactive Recommenders}, year = {2024}, isbn = {9798400704864}, publisher = {International Foundation for Autonomous Agents and Multiagent Systems}, address = {Richland, SC}, abstract = {With the pervasive usage of recommendation systems across various domains, there is a growing need for transparent and convincing interactions to build a rapport with the system users. Incorporating explainability into recommendation systems has become a promising strategy to bolster user trust and sociability. This study centers on recommendation systems that leverage varying explainability techniques to cultivate trust by delivering comprehensible customized explanations for the given recommendations. Accordingly, we propose two explanation methods aligning with a cluster-based recommendation strategy.}, booktitle = {Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems}, pages = {2174--2176}, numpages = {3}, keywords = {explainable recommendation, explanation strategy}, location = {Auckland, New Zealand}, series = {AAMAS '24} }

Abstract: With the pervasive usage of recommendation systems across various domains, there is a growing need for transparent and convincing interactions to build a rapport with the system users. Incorporating explainability into recommendation systems has become a promising strategy to bolster user trust and sociability. This study centers on recommendation systems that leverage varying explainability techniques to cultivate trust by delivering comprehensible customized explanations for the given recommendations. Accordingly, we propose two explanation methods aligning with a cluster-based recommendation strategy.

Cite As: Berk Buzcu, Emre Kuru, and Reyhan Aydoğan, User-centric Explanation Strategies for Interactive Recommenders, AAMAS 2024, Extended Abstract, New Zealand, Auckland, 2024

Type: Conference

Links: https://www.ijcai.org/proceedings/2024/1012.pdf

Bibtex: @inproceedings{Keskin_IJCAI_2024, title={NEGOTIATOR: A Comprehensive Framework for Human-Agent Negotiation Integrating Preferences, Interaction, and Emotion}, author={Keskin, Mehmet Onur and Buzcu, Berk and Koc{c}yigit, Berkecan and c{C}akan, Umut and Dou{g}ru, An{i}l and Aydou{g}an, Reyhan}, booktitle={International Joint Conference on Artificial Intelligence}, year={2024}, url={https://api.semanticscholar.org/CorpusID:271504971} }

Abstract: The paper introduces a comprehensive humanagent negotiation framework designed to facilitate the development and evaluation of research studies on human-agent negotiation without building each component from scratch. Leveraging the interoperability and reusability of its components, this framework offers various functionalities, including speech-to-text conversion, emotion recognition, a repository of negotiation strategies, and an interaction manager capable of managing gestures designed for Nao, Pepper, and QT, and coordinating message exchanges in a turn-taking fashion. This framework aims to lower the entry barrier for researchers in human-agent negotiation by providing a versatile platform that supports a wide range of research directions, including affective computing, natural language processing, decision-making, and non-verbal communication.

Cite As: Mehmet Onur Keskin, Berk Buzcu, Berkecan Koçyiğit, Umut Çakan, Anıl Doğru, Reyhan Aydoğan,“NEGOTIATOR: A Comprehensive Framework for Human-Agent Negotiation Integrating Preferences, Interaction, and Emotion”, the Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI), Demo Track. pp.8640-8643, Jeju, South Korea, 2024

Type: Conference

Links: https://www.ijcai.org/proceedings/2024/998

Bibtex: @inproceedings{Dogru_IJCAI_2024, title = {NegoLog: An Integrated Python-based Automated Negotiation Framework with Enhanced Assessment Components}, author = {Dou{g}ru, An{i}l and Keskin, Mehmet Onur and Jonker, Catholijn M. and Baarslag, Tim and Aydou{g}an, Reyhan}, booktitle = {Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, {IJCAI-24}}, publisher = {International Joint Conferences on Artificial Intelligence Organization}, editor = {Larson, Kate}, pages = {8640--8643}, year = {2024}, month = {8}, note = {Demo Track}, doi = {10.24963/ijcai.2024/998}, url = {https://doi.org/10.24963/ijcai.2024/998}, }

Abstract: The complexity of automated negotiation research calls for dedicated, user-friendly research frameworks that facilitate advanced analytics, comprehensive loggers, visualization tools, and auto-generated domains and preference profiles. This paper introduces NegoLog, a platform that provides advanced and customizable analysis modules to agent developers for exhaustive performance evaluation. NegoLog introduces an automated scenario and tournament generation tool in its Web-based user interface so that the agent developers can adjust the competitiveness and complexity of the negotiations. One of the key novelties of the NegoLog is an individual assessment of preference estimation models independent of the strategies.

Cite As: Anıl Doğru, Mehmet Onur Keskin, Catholijn M. Jonker, Tim Baarslag, Reyhan Aydoğan, “NegoLog: An Integrated Python-based Automated Negotiation Framework with Enhanced Assessment Components”, the Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI), Demo Track. pp.8640-8643, Jeju, South Korea, 2024

Type: Conference

Links: https://dl.acm.org/doi/10.1145/3570945.3607362

Bibtex: @inproceedings{Cakan_IVA_2023, author = {c{C}akan, Umut and Keskin, M. Onur and Aydou{g}an, Reyhan}, title = {Effects of Agent's Embodiment in Human-Agent Negotiations}, year = {2023}, isbn = {9781450399944}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3570945.3607362}, doi = {10.1145/3570945.3607362}, abstract = {Human-agent negotiation has recently attracted researchers' attention due to its complex nature and potential usage in daily life scenarios. While designing intelligent negotiating agents, they mainly focus on the interaction protocol (i.e., what to exchange and how) and strategy (i.e., how to generate offers and when to accept). Apart from these components, the embodiment may implicitly influence the negotiation process and outcome. The perception of a physically embodied agent might differ from the virtually embodied one; thus, it might influence human negotiators' decisions and responses. Accordingly, this work empirically studies the effect of physical and virtual embodiment in human-agent negotiations. We designed and conducted experiments where human participants negotiate with a humanoid robot in one setting, whereas they negotiate with a virtually embodied replica of that robot in another setting. The experimental results showed that social welfare was statistically significantly higher when the negotiation was held with a virtually embodied robot rather than a physical robot. Human participants took the negotiation more seriously against physically embodied agents and made more collaborative moves in the virtual setting. Furthermore, their survey responses indicate that participants perceived our robot as more humanlike when it is physically embodied.}, booktitle = {Proceedings of the 23rd ACM International Conference on Intelligent Virtual Agents}, articleno = {11}, numpages = {8}, keywords = {Embodiment, Human-Agent Negotiation, Human-Robot Interaction}, location = {W{"u}rzburg, Germany}, series = {IVA '23} }

Abstract: Human-agent negotiation has recently attracted researchers' attention due to its complex nature and potential usage in daily life scenarios. While designing intelligent negotiating agents, they mainly focus on the interaction protocol (i.e., what to exchange and how) and strategy (i.e., how to generate offers and when to accept). Apart from these components, the embodiment may implicitly influence the negotiation process and outcome. The perception of a physically embodied agent might differ from the virtually embodied one; thus, it might influence human negotiators' decisions and responses. Accordingly, this work empirically studies the effect of physical and virtual embodiment in human-agent negotiations. We designed and conducted experiments where human participants negotiate with a humanoid robot in one setting, whereas they negotiate with a virtually embodied replica of that robot in another setting. The experimental results showed that social welfare was statistically significantly higher when the negotiation was held with a virtually embodied robot rather than a physical robot. Human participants took the negotiation more seriously against physically embodied agents and made more collaborative moves in the virtual setting. Furthermore, their survey responses indicate that participants perceived our robot as more humanlike when it is physically embodied.

Cite As: Umut Çakan, Mehmet Onur Keskin, and Reyhan Aydoğan, “Effects of Agent's Embodiment in Human-Agent Negotiations”, 23rd ACM International Conference on Intelligent Virtual Agents, Accepted, 2023.

Topic: Computing methodologies, Artificial intelligence, Distributed artificial intelligence, Intelligent agents

Type: Conference

Links: https://ec-3.org/publications/conference/paper/?id=EC32023_291

Bibtex: @inproceedings{Bolluk_EC3_2023, doi = {10.35490/EC3.2023.291}, url = {https://ec-3.org/publications/conference/paper/?id=EC32023_291}, year = {2023}, month = {July}, publisher = {European Council on Computing in Construction}, author = {Bolluk, Muhammed Said and Seyis, Senem and Aydou{g}an, Reyhan}, title = {Feature extraction for enhancing data-driven urban building energy models}, booktitle = {Proceedings of the 2023 European Conference on Computing in Construction and the 40th International CIB W78 Conference}, volume = {4}, isbn = {978-0-701702-73-1}, address = {Heraklion, Greece}, series = {Computing in Construction}, language = {en-GB}, abstract = {Building energy demand assessment plays a crucial role in designing energy-efficient building stocks. However, most data-driven studies feel the deficiency of datasets with building-specific information in building energy consumption estimation. Hence, the research objective of this study is to extract new features within the climate, demographic, and building use type categories and increase the accuracy of a non-parametric regression model estimating the energy consumption of a building stock in Seattle. The results show that adding new features to the original dataset from the building use type category increased the regression results with a 6.8% less error and a 30.8% higher R2 Score.}, issn = {2684-1150}, Organisation = {European Conference on Computing in Construction}, Editors = {Mohamad Kassem, Lavinia Chiara Tagliabue, Robert Amor, Marijana Sreckovic, Athanasios Chassiakos } }

Abstract: Building energy demand assessment plays a crucial role in designing energy-efficient building stocks. However, most data-driven studies feel the deficiency of datasets with building-specific information in building energy consumption estimation. Hence, the research objective of this study is to extract new features within the climate, demographic, and building use type categories and increase the accuracy of a non-parametric regression model estimating the energy consumption of a building stock in Seattle. The results show that adding new features to the original dataset from the building use type category increased the regression results with a 6.8% less error and a 30.8% higher R2 Score.

Cite As: Said Bolluk, Senem Seyis, and Reyhan Aydoğan, “Feature extraction for enhancing data-driven urban building energy models”, EC3 Conference 2023,European Council on Computing in Construction, 2023

Type: Conference

Links: https://link.springer.com/chapter/10.1007/978-3-031-21203-1_23

Bibtex: @inproceedings{Yesevi_PRIMA_2023, author = {Yesevi, Gevher and Keskin, Mehmet Onur and Dou{g}ru, An{i}l and Aydou{g}an, Reyhan}, editor = {Aydou{g}an, Reyhan and Criado, Natalia and Lang, J{'e}r{^o}me and Sanchez-Anguix, Victor and Serramia, Marc}, title = {Time Series Predictive Models for Opponent Behavior Modeling in Bilateral Negotiations}, booktitle = {PRIMA 2022: Principles and Practice of Multi-Agent Systems}, year = {2023}, publisher = {Springer International Publishing}, address = {Cham}, pages = {381--398}, abstract = {In agent-based negotiations, it is crucial to understand the opponent's behavior and predict its bidding pattern to act strategically. Foreseeing the utility of the opponent's coming offer provides valuable insight to the agent so that it can decide its next move wisely. Accordingly, this paper addresses predicting the opponent's coming offers by employing two deep learning-based approaches: Long Short-Term Memory Networks and Transformers. The learning process has three different targets: estimating the agent's utility of the opponent's coming offer, estimating the agent's utility of that without using opponent-related variables, and estimating the opponent's utility of that by using opponent-related variables. This work reports the performances of these models that are evaluated in various negotiation scenarios. Our evaluation showed promising results regarding the prediction performance of the proposed methods.}, isbn = {978-3-031-21203-1} }

Abstract: In agent-based negotiations, it is crucial to understand the opponent’s behavior and predict its bidding pattern to act strategically. Foreseeing the utility of the opponent’s coming offer provides valuable insight to the agent so that it can decide its next move wisely. Accordingly, this paper addresses predicting the opponent’s coming offers by employing two deep learning-based approaches: Long Short-Term Memory Networks and Transformers. The learning process has three different targets: estimating the agent’s utility of the opponent’s coming offer, estimating the agent’s utility of that without using opponent-related variables, and estimating the opponent’s utility of that by using opponent-related variables. This work reports the performances of these models that are evaluated in various negotiation scenarios. Our evaluation showed promising results regarding the prediction performance of the proposed methods.

Cite As: Gevher Yesevi, Mehmet Onur Keskin, Anıl Doğru, and Reyhan Aydoğan, “Time Series Predictive Models for Opponent Behavior Modeling in Bilateral Negotiations”, The 24th International Conference on Principles and Practice of Multi-Agent Systems, November, Valencia, 2022.

Topic: Automated negotiation, Multi-agent systems, Time-series prediction, Utility prediction

Type: Conference

Links: https://link.springer.com/chapter/10.1007/978-3-031-21203-1_2

Bibtex: @inproceedings{Buzcu_PRIMA_2023, author = {Buzcu, Berk and Varadhajaran, Vanitha and Tchappi, Igor and Najjar, Amro and Calvaresi, Davide and Aydou{g}an, Reyhan}, editor = {Aydou{g}an, Reyhan and Criado, Natalia and Lang, J{'e}r{^o}me and Sanchez-Anguix, Victor and Serramia, Marc}, title = {Explanation-Based Negotiation Protocol for Nutrition Virtual Coaching}, booktitle = {PRIMA 2022: Principles and Practice of Multi-Agent Systems}, year = {2023}, publisher = {Springer International Publishing}, address = {Cham}, pages = {20--36}, abstract = {People's awareness about the importance of healthy lifestyles is rising. This opens new possibilities for personalized intelligent health and coaching applications. In particular, there is a need for more than simple recommendations and mechanistic interactions. Recent studies have identified nutrition virtual coaching systems (NVC) as a technological solution, possibly bridging technologies such as recommender, informative, persuasive, and argumentation systems. Enabling NVC to explain recommendations and discuss (argument) dietary solutions and alternative items or behaviors is crucial to improve the transparency of these applications and enhance user acceptability and retain their engagement. This study primarily focuses on virtual agents personalizing the generation of food recipes recommendation according to users' allergies, eating habits, lifestyles, nutritional values, etc. Although the agent would nudge the user to consume healthier food, users may tend to object in favor of tastier food. To resolve this divergence, we propose a user-agent negotiation interacting over the revision of the recommendation (via feedback and explanations) or convincing (via explainable arguments) the user of its benefits and importance. Finally, the paper presents our initial findings on the acceptability and usability of such a system obtained via tests with real users. Our preliminary experimental results show that the majority of the participants appreciate the ability to express their feedback as well as receive explanations of the recommendations, while there is still room for improvement in the persuasiveness of the explanations.}, isbn = {978-3-031-21203-1} }

Abstract: People’s awareness about the importance of healthy lifestyles is rising. This opens new possibilities for personalized intelligent health and coaching applications. In particular, there is a need for more than simple recommendations and mechanistic interactions. Recent studies have identified nutrition virtual coaching systems (NVC) as a technological solution, possibly bridging technologies such as recommender, informative, persuasive, and argumentation systems. Enabling NVC to explain recommendations and discuss (argument) dietary solutions and alternative items or behaviors is crucial to improve the transparency of these applications and enhance user acceptability and retain their engagement. This study primarily focuses on virtual agents personalizing the generation of food recipes recommendation according to users’ allergies, eating habits, lifestyles, nutritional values, etc. Although the agent would nudge the user to consume healthier food, users may tend to object in favor of tastier food. To resolve this divergence, we propose a user-agent negotiation interacting over the revision of the recommendation (via feedback and explanations) or convincing (via explainable arguments) the user of its benefits and importance. Finally, the paper presents our initial findings on the acceptability and usability of such a system obtained via tests with real users. Our preliminary experimental results show that the majority of the participants appreciate the ability to express their feedback as well as receive explanations of the recommendations, while there is still room for improvement in the persuasiveness of the explanations.

Cite As: Berk Buzcu, Vanitha Varadhajaran, Igor Tchappi, Amro Najjar, Davide Calvaresi and Reyhan Aydoğan, “Explanation-based Negotiation Protocol for Nutrition Virtual Coaching”, The 24th International Conference on Principles and Practice of Multi-Agent Systems, November, Valencia, 2022.

Topic: Explainable AI, Recommender systems, Interactive, Nutrition virtual coach

Type: Conference

Links: https://dl.acm.org/doi/10.1145/3486622.3494023

Bibtex: @inproceedings{Baarslag_ACM_2021, author = {Baarslag, Tim and Elfrink, Tijmen and Nassiri-Mofakham, Faria and Koca, Thimjo and Kaisers, Michael and Aydou{g}an, Reyhan}, title = {Bargaining Chips: Coordinating One-to-Many Concurrent Composite Negotiations}, year = {2022}, isbn = {9781450391153}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3486622.3494023}, doi = {10.1145/3486622.3494023}, abstract = {This study presents Bargaining Chips: a framework for one-to-many concurrent composite negotiations, where multiple deals can be reached and combined. Our framework is designed to mirror the salient aspects of real-life procurement and trading scenarios, in which a buyer seeks to acquire a number of items from different sellers at the same time. To do so, the buyer needs to successfully perform multiple concurrent bilateral negotiations as well as coordinate the composite outcome resulting from each interdependent negotiation. This paper contributes to the state of the art by: (1) presenting a model and test-bed for addressing such challenges; (2) by proposing a new, asynchronous interaction protocol for coordinating concurrent negotiation threads; and (3) by providing classes of multi-deal coordinators that are able to navigate this new one-to-many multi-deal setting. We show that Bargaining Chips can be used to evaluate general asynchronous negotiation and coordination strategies in a setting that generalizes over a number of existing negotiation approaches.}, booktitle = {IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology}, pages = {390-397}, numpages = {8}, keywords = {procurement, multi-deal, coordination, concurrent negotiations, composite negotiations, asynchronous offers, One-to-many negotiations}, location = {Melbourne, VIC, Australia}, series = {WI-IAT '21} }

Abstract: This study presents Bargaining Chips: a framework for one-tomany concurrent composite negotiations, where multiple deals can be reached and combined. Our framework is designed to mirror the salient aspects of real-life procurement and trading scenarios, in which a buyer seeks to acquire a number of items from different sellers at the same time. To do so, the buyer needs to successfully perform multiple concurrent bilateral negotiations as well as coordinate the composite outcome resulting from each interdependent negotiation. This paper contributes to the state of the art by: (1) presenting a model and test-bed for addressing such challenges; (2) by proposing a new, asynchronous interaction protocol for coordinating concurrent negotiation threads; and (3) by providing classes of multi-deal coordinators that are able to navigate this new one-to-many multi-deal setting. We show that Bargaining Chips can be used to evaluate general asynchronous negotiation and coordination strategies in a setting that generalizes over a number of existing negotiation approaches.

Cite As: Tim Baarslag, Tijmen Elfrink, Faria Nassiri Mofakham, Thimjo Koça, Michael Kaisers, and Reyhan Aydogan, "Bargaining Chips: Coordinating One-to-Many Concurrent Composite Negotiations", IEEE WI-IAT 2021 conference, Melbourne, Australia, December, 2021.

Topic: One-to-many negotiations, multi-deal, composite negotiations, concurrent negotiations, procurement, asynchronous offers, coordination

Type: Conference

Links: https://luis.leiva.name/tmp/bnaic2021_preproceedings.pdf

Bibtex: @inproceedings{Contreras_BNAIC_2021, title = {On Explainable Negotiations via Argumentation}, author = {Contreras, Victor and Aydou{g}an, Reyhan and Najjar, Amro and Calvaresi, Davide}, year = {2021}, month = {November}, booktitle = {33rd Benelux Conference on Artificial Intelligence, BNAIC}, address = {Esch-sur-Alzette, Luxembourg}, series = {BNAIC/Benelearn: Benelux Conference on Artificial Intelligence}, volume = {}, number = {}, pages = {680--682}, }

Abstract: Modern society performs countless choices affecting all sorts of needs daily. Bothindustry and academia are intensifying their effort to both extend the plethoraof possible alternatives and narrow down the most significant ones to be sug-gested to the user [1]. Thus, it would maximize the possibility of the servicesconsumption and user satisfaction. Recommender systems (RS) [2] have reachedremarkable accuracy and efficacy in several domains [3]. Nevertheless, more sen-sitive areas (i.e., nutrition) demand more complex dynamics beyond conventionalRS’ capabilities. For example, virtual coaching systems (VCS) leverage persua-sion techniques, argumentation, informative systems, and RS (see Figure 1a).However, their efficacy is still far from the one achieved by human coaches, evenconsidering the limitations of the case (see [4]). In particular, the major setbacksare the lack of explanations supporting a given suggestion, the impossibility of“discussing” it with the VCS, and the lack of significant explorations for newout-of-the-box solutions.Therefore, this work suggests the following negotiation schema for nutritionVCS: 1 −to −1(−to −σ) with σ= 0, ..., N and Nbeing the number of virtualVCs in the system. In particular, it leverages human-to-agent (1 −to −1) andagent-to-agent (1 −to −σ) joint problem solving via negotiation to generaterecommendations and arguments to support them.

Cite As: Victor Contreras, Reyhan Aydogan, Amro Najjar and Davide Calvaresi, On Explainable Negotiations via Argumentation, BNAC/BeneLearn 2021, Luxembourg, pp. 680-682, November 2021.

Topic: Recommender Systems, Virtual Coaching Systems, Persuasion Techniques, Argumentation

Type: Conference

Links: https://dl.acm.org/doi/10.5555/3463952.3464158

Bibtex: @inproceedings{Keskin_AAMAS_2021, author = {Keskin, Mehmet Onur and c{C}akan, Umut and Aydou{g}an, Reyhan}, title = {Solver Agent: Towards Emotional and Opponent-Aware Agent for Human-Robot Negotiation}, year = {2021}, isbn = {9781450383073}, publisher = {International Foundation for Autonomous Agents and Multiagent Systems}, address = {Richland, SC}, abstract = {Negotiation is one of the crucial processes for resolving conflicts between parties. In automated negotiation, agent designers mostly take opponent's offers and the remaining time into account while designing their strategies. While designing a negotiating agent interacting with a human directly, other information such as opponent's emotional changes during the negotiation can establish a better interaction and reach an admissible settlement for joint interests. Accordingly, this paper proposes a bidding strategy for humanoid robots, which incorporates their opponents' emotional states and awareness of the agent's changing behavior.}, booktitle = {Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems}, pages = {1557--1559}, numpages = {3}, keywords = {emotional awareness, human-agent negotiation, human-robot interaction, human-robot negotiation, negotiation strategy, opponent behavior}, location = {Virtual Event, United Kingdom}, series = {AAMAS '21} }

Abstract: Negotiation is one of the crucial processes for resolving conflicts between parties. In automated negotiation, agent designers mostly take opponent's offers and the remaining time into account while designing their strategies. While designing a negotiating agent interacting with a human directly, other information such as opponent's emotional changes during the negotiation can establish a better interaction and reach an admissible settlement for joint interests. Accordingly, this paper proposes a bidding strategy for humanoid robots, which incorporates their opponents' emotional states and awareness of the agent's changing behavior.

Cite As: Mehmet Onur Keskin, Umut Çakan and Reyhan Aydoğan, “Solver Agent: Towards Emotional and Opponent-Aware Agent for Human-Robot Negotiation”, Accepted at the 20th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021, pp.1557-1559, 2021.

Topic: Human-Agent Negotiation, Emotional Awareness, Opponent Be-havior, Human-Robot Interaction

Type: Conference

Links: https://link.springer.com/chapter/10.1007/978-3-030-82254-5_16

Bibtex: @inproceedings{Eran_MAS_2021, author = {Eran, Cihan and Keskin, M. Onur and Cant"{u}rk, Furkan and Aydou{g}an, Reyhan}, editor = {Rosenfeld, Ariel and Talmon, Nimrod}, title = {A Decentralized Token-Based Negotiation Approach for Multi-Agent Path Finding}, booktitle = {Multi-Agent Systems}, year = {2021}, publisher = {Springer International Publishing}, address = {Cham}, pages = {264--280}, abstract = {This paper introduces a negotiation approach to solve the Multi-Agent Path Finding problem. The approach aims to achieve a good trade-off between the privacy of the agents and the effectiveness of solutions. Accordingly, a token-based bilateral negotiation protocol and a compatible negotiation strategy are presented. The proposed approach is evaluated in a variety of scenarios by comparing it with state-of-the-art centralized approaches such as Conflict Based Search and its variant. The experimental results showed that the proposed approach can find conflict-free path solutions with a higher success rate, especially when the search space is large and high-density compared to centralized approaches while the gap between path cost differences is reasonably low. The proposed approach enables agents to have their autonomy; thus, it is convenient for MAPF problems involving self-interested agents.}, isbn = {978-3-030-82254-5} }

Abstract: This paper introduces a negotiation approach to solve the Multi-Agent Path Finding problem. The approach aims to achieve a good trade-off between the privacy of the agents and the effectiveness of solutions. Accordingly, a token-based bilateral negotiation protocol and a compatible negotiation strategy are presented. The proposed approach is evaluated in a variety of scenarios by comparing it with state-of-the-art centralized approaches such as Conflict Based Search and its variant. The experimental results showed that the proposed approach can find conflict-free path solutions with a higher success rate, especially when the search space is large and high-density compared to centralized approaches while the gap between path cost differences is reasonably low. The proposed approach enables agents to have their autonomy; thus, it is convenient for MAPF problems involving self-interested agents.

Cite As: Cihan Eran, Onur Keskin, Furkan Canturk and Reyhan Aydoğan, “A Decentralized Token-based Negotiation Approach for Multi-Agent Path Finding”,The 18th European Conference on Multi-Agent Systems (EUMAS), pp 264-280, July, 2021.

Topic: Multi-Agent Path Finding, Negotiation, Decentralized coordination, Self-interested agents

Type: Conference

Links: https://dl.acm.org/doi/10.1145/3449726.3463219

Bibtex: @inproceedings{Tunali_GECCO_2021, author = {Tunal{i}, Okan and Bayrak, Ahmet Tuu{g}rul and Sanchez-Anguix, Victor and Aydou{g}an, Reyhan}, title = {Multi-objective evolutionary product bundling: a case study}, year = {2021}, isbn = {9781450383516}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3449726.3463219}, doi = {10.1145/3449726.3463219}, abstract = {Product bundling is a strategy conducted by marketing decision-makers to combine items or services for targeted sales in today's competitive business environment. Targeted sales can be in various forms, like increasing the likelihood of a purchase, promoting some products among a specific customer segment, or improving user experience. In this study, we propose an evolutionary product bundle generation strategy that is based on the NSGA-II algorithm. The proposed approach is designed as a multi-objective optimization procedure where the objectives are designed in terms of desired bundle feature distributions. The designed genetic algorithm is flexible and allows decision-makers to specify objectives such as price, season, item similarity and association with bundle size constraints. In the experiments, we show that the evolutionary approach enables us to generate Pareto solutions compared to the initial population.}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion}, pages = {1622--1629}, numpages = {8}, keywords = {bundle generation, decision support systems, evolutionary algorithms, genetic algorithm, multi-objective optimization}, location = {Lille, France}, series = {GECCO '21} }

Abstract: Product bundling is a strategy conducted by marketing decision-makers to combine items or services for targeted sales in today's competitive business environment. Targeted sales can be in various forms, like increasing the likelihood of a purchase, promoting some products among a specific customer segment, or improving user experience. In this study, we propose an evolutionary product bundle generation strategy that is based on the NSGA-II algorithm. The proposed approach is designed as a multi-objective optimization procedure where the objectives are designed in terms of desired bundle feature distributions. The designed genetic algorithm is flexible and allows decision-makers to specify objectives such as price, season, item similarity and association with bundle size constraints. In the experiments, we show that the evolutionary approach enables us to generate Pareto solutions compared to the initial population.

Cite As: Okan Tunalı, Ahmet Tuğrul Bayrak, Victor Sanchez-Anguix and Reyhan Aydoğan, “Multi-Objective Evolutionary Product Bundling: A Case Study”, Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO ’21 series, pp. 1622–1629, Association for Computing Machinery, Lille, France, 2021.

Topic: bundle generation, decision support systems, evolutionary algorithms, genetic algorithm, multi-objective optimization

Type: Conference

Links: https://openaccess.thecvf.com/content/WACV2021/papers/Sensoy_Misclassification_Risk_and_Uncertainty_Quantification_in_Deep_Classifiers_WACV_2021_paper.pdf

Bibtex: @inproceedings{Sensoy_WACV_2021, author = {Sensoy, Murat and Saleki, Maryam and Julier, Simon and Aydou{g}an, Reyhan and Reid, John}, booktitle = {2021 IEEE Winter Conference on Applications of Computer Vision (WACV)}, title = {Misclassification Risk and Uncertainty Quantification in Deep Classifiers}, year = {2021}, volume = {}, number = {}, pages = {2483-2491}, keywords = {Training;Deep learning;Computer vision;Uncertainty;Conferences;Decision making;Predictive models}, doi = {10.1109/WACV48630.2021.00253} }

Abstract: In this paper, we propose risk-calibrated evidential deep classifiers to reduce the costs associated with classification errors. We use two main approaches. The first is to develop methods to quantify the uncertainty of a classifier’s predictions and reduce the likelihood of acting on erroneous predictions. The second is a novel way to train the classifier such that erroneous classifications are biased towards less risky categories. We combine these two approaches in a principled way. While doing this, we extend evidential deep learning with pignistic probabilities, which are used to quantify uncertainty of classification predictions and model rational decision making under uncertainty. We evaluate the performance of our approach on several image classification tasks. We demonstrate that our approach allows to (i) incorporate misclassification cost while training deep classifiers, (ii) accurately quantify the uncertainty of classification predictions, and (iii) simultaneously learn how to make classification decisions to minimize expected cost of classification errors.

Cite As: Murat Sensoy, Maryam Saleki, Simon Julier, Reyhan Aydoğan, John Reid, “Misclassification Risk and Uncertainty Quantification in Deep Classifiers”, Accepted at Winter Conference on Applications of Computer Vision 2021, WACV 2021, January 5-9, 2021.

Type: Conference

Links: https://dl.acm.org/doi/abs/10.5555/3398761.3399053

Bibtex: @inproceedings{Sensoy_AAMAS_2020, author = {Sensoy, Murat and Saleki, Maryam and Julier, Simon and Aydou{g}an, Reyhan and Reid, John}, title = {Not all Mistakes are Equal}, year = {2020}, isbn = {9781450375184}, publisher = {International Foundation for Autonomous Agents and Multiagent Systems}, address = {Richland, SC}, abstract = {In many tasks, classifiers play a fundamental role in the way an agent behaves. Most rational agents collect sensor data from the environment, classify it, and act based on that classification. Recently, deep neural networks (DNNs) have become the dominant approach to develop classifiers due to their excellent performance. When training and evaluating the performance of DNNs, it is normally assumed that the cost of all misclassification errors are equal. However, this is unlikely to be true in practice. Incorrect classification predictions can cause an agent to take inappropriate actions. The costs of these actions can be asymmetric, vary from agent-to-agent, and depend on context.In this paper, we discuss the importance of considering risk and uncertainty quantification together to reduce agents' cost of making misclassifications using deep classifiers.}, booktitle = {Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems}, pages = {1996--1998}, numpages = {3}, keywords = {cost-sensitive learning, deep learning, risk, uncertainty}, location = {Auckland, New Zealand}, series = {AAMAS '20} }

Abstract: The Autonomous Agents and Multiagent Systems (AAMAS) conference series gathers researchers from around the world to share the latest advances in the field. It is the premier forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS 2002, the first of the series, was held in Bologna, followed by Melbourne (2003), New York (2004), Utrecht (2005), Hakodate (2006), Honolulu (2007), Estoril (2008), Budapest (2009), Toronto (2010), Taipei (2011), Valencia (2012), Saint Paul (2013), Paris (2014), Istanbul (2015), Singapore (2016), São Paulo (2017), Stockholm (2018) and Montreal (2019). This volume is the proceedings of AAMAS 2020, the 19th conference in the series, which was to be held in Auckland in May 2020. AAMAS 2020 invited submissions for a general track, a Blue Sky Ideas track, and a track to present papers from JAAMAS (the journal Autonomous Agents and Multi-Agent Systems) that had not previously been presented at a major conference. The Blue Sky Ideas track was chaired by Alessandro Ricci and Juan Antonio Rodriguez. Rym Zalila-Wenkstern and P?nar Yolum solicited papers for the JAAMAS Presentation Track from the papers that appeared in JAAMAS within the preceding 12 months. A group of Area Chairs (AC) was selected to help oversee the review process of the main track. The ACs performed an initial check of submissions and recommended summary rejection of those that did not meet the AAMAS scope, submission or formatting instructions. Jointly with the program chairs, the chairs of the ten areas were responsible for appointing Senior Program Committee (SPC) members, who in turn helped identify a strong and diverse set of Program Committee (PC) members. PC could review for more than one area. Every paper was reviewed by at least three PC members, overseen by an SPC member who ensured reviews were clear and informative. After authors were given an opportunity to respond to the reviewers, the SPC member led a discussion where the reviewers considered each others', and the authors', comments. The area chairs in turn worked with the program chairs to make final decisions about acceptance for the papers, to ensure uniformly high quality.

Cite As: Murat Sensoy, Maryam Saleki, Simon Julier, Reyhan Aydoğan and John Reid, “Not all Mistakes are Equal”, In the Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS), pp. 1996-1998, New Zealand, 2020.

Type: Conference

Links: https://link.springer.com/chapter/10.1007/978-3-030-66412-1_23

Bibtex: @inproceedings{Aydogan_MASAT_2020, author = {Aydou{g}an, Reyhan and Baarslag, Tim and Fujita, Katsuhide and Mell, Johnathan and Gratch, Jonathan and de Jonge, Dave and Mohammad, Yasser and Nakadai, Shinji and Morinaga, Satoshi and Osawa, Hirotaka and Aranha, Claus and Jonker, Catholijn M.}, editor = {Bassiliades, Nick and Chalkiadakis, Georgios and de Jonge, Dave}, title = {Challenges and Main Results of the Automated Negotiating Agents Competition (ANAC) 2019}, booktitle = {Multi-Agent Systems and Agreement Technologies}, year = {2020}, publisher = {Springer International Publishing}, address = {Cham}, pages = {366--381}, abstract = {The Automated Negotiating Agents Competition (ANAC) is a yearly-organized international contest in which participants from all over the world develop intelligent negotiating agents for a variety of negotiation problems. To facilitate the research on agent-based negotiation, the organizers introduce new research challenges every year. ANAC 2019 posed five negotiation challenges: automated negotiation with partial preferences, repeated human-agent negotiation, negotiation in supply-chain management, negotiating in the strategic game of Diplomacy, and in the Werewolf game. This paper introduces the challenges and discusses the main findings and lessons learnt per league.}, isbn = {978-3-030-66412-1} }

Abstract: The Automated Negotiating Agents Competition (ANAC) is a yearly-organized international contest in which participants from all over the world develop intelligent negotiating agents for a variety of negotiation problems. To facilitate the research on agent-based negotiation, the organizers introduce new research challenges every year. ANAC 2019 posed five negotiation challenges: automated negotiation with partial preferences, repeated human-agent negotiation, negotiation in supply-chain management, negotiating in the strategic game of Diplomacy, and in the Werewolf game. This paper introduces the challenges and discusses the main findings and lessons learnt per league.

Cite As: Reyhan Aydoğan, Tim Baarslag, Katsuhide Fujita, Johnathan Mell, Jonathan Gratch, Dave de Jonge, Yasser Mohammad, Shinji Nakadai, Satoshi Morinaga, Hirotaka Osawa, Claus Aranha, and Catholijn Jonker, “Challenges and Main Results of the Automated Negotiating Agents Competition (ANAC) 2019″, The Seventh International Conference on Agreement Technologies, April, Greece, 2020.

Type: Conference

Links: https://arc.aiaa.org/doi/10.2514/6.2020-0389

Bibtex: @inproceedings{Bouarfa_AIAA_2020, author = {Bouarfa, Soufiane and Dou{g}ru, An{i}l and Arizar, Ridwan and Aydou{g}an, Reyhan and Serafico, Joselito}, title = {Towards Automated Aircraft Maintenance Inspection. A use case of detecting aircraft dents using Mask R-CNN}, booktitle = {AIAA Scitech 2020 Forum}, chapter = {}, pages = {}, year = {2020}, doi = {10.2514/6.2020-0389}, URL = {https://arc.aiaa.org/doi/abs/10.2514/6.2020-0389}, eprint = {https://arc.aiaa.org/doi/pdf/10.2514/6.2020-0389}, abstract = { Deep learning can be used to automate aircraft maintenance visual inspection. This can help increase the accuracy of damage detection, reduce aircraft downtime, and help prevent inspection accidents. The objective of this paper is to demonstrate the potential of this method in supporting aircraft engineers to automatically detect aircraft dents. The novelty of the work lies in applying a recently developed neural network architecture know by Mask R-CNN, which enables the detection of objects in an image while simultaneously generating a segmentation mask for each instance. Despite the small dataset size used for training, the results are promising and demonstrate the potential of deep learning to automate aircraft maintenance inspection. The model can be trained to identify additional types of damage such as lightning strike entry and exit points, paint damage, cracks and holes, missing markings, and can therefore be a useful decision-support system for aircraft engineers. } }

Abstract: Deep learning can be used to automate aircraft maintenance visual inspection. This can help increase the accuracy of damage detection, reduce aircraft downtime, and help prevent inspection accidents. The objective of this paper is to demonstrate the potential of this method in supporting aircraft engineers to automatically detect aircraft dents. The novelty of the work lies in applying a recently developed neural network architecture know by Mask R-CNN, which enables the detection of objects in an image while simultaneously generating a segmentation mask for each instance. Despite the small dataset size used for training, the results are promising and demonstrate the potential of deep learning to automate aircraft maintenance inspection. The model can be trained to identify additional types of damage such as lightning strike entry and exit points, paint damage, cracks and holes, missing markings, and can therefore be a useful decision-support system for aircraft engineers.

Cite As: Soufiane Bouarfa, Anil Doğru, Ridwan Arizar, Reyhan Aydoğan and Joselito Serafico, “Towards Automated Aircraft Maintenance Inspection. A use case of detecting aircraft dents using Mask R-CNN”, SCI-Tech Forum AIAA – American Institute of Aeronautics and Astronautics, Orlando, January 2020.

Type: Conference

Links: https://www.insticc.org/Primoris/Resources/PaperPdf.ashx?idPaper=89769

Bibtex: @inproceedings{Yavuz_SCM_2020, title = "Taking inventory changes into account while negotiating in supply chain management", abstract = "In a supply chain environment, supply chain entities need to make joint decisions on the transaction of goods under the issues quantity, delivery time and unit price in order to procure/sell goods at right quantities and time while minimizing the transaction costs. This paper presents our negotiating agent designed for Supply Chain Management League (SCML) in the International Automated Negotiation Agents Competition (ANAC). Basically, the proposed approach relies on determining reservation value by taking the changes in the inventory stock into account. We have tested the performance of our bidding strategy in the competition simulation environment and compared it with the performance of the winner strategies in ANAC SCML 2019. Our experimental results showed that the proposed strategy outperformed the winner strategies in overall.", keywords = "Agent-based Negotiation, Bidding Strategy, Supply Chain Management", author = "{Berk Yavuz}, {Celal Ozan} and {c C}au{g}il S{"u}sl{"u} and Reyhan Aydou{g}an", year = "2020", language = "English", volume = "1", series = "ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence", publisher = "SciTePress", pages = "94--103", editor = "Ana Rocha and Luc Steels and {van den Herik}, Jaap", booktitle = "ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence", note = "12th International Conference on Agents and Artificial Intelligence, ICAART 2020 ; Conference date: 22-02-2020 Through 24-02-2020", }

Abstract: In a supply chain environment, supply chain entities need to make joint decisions on the transaction of goods under the issues quantity, delivery time and unit price in order to procure/sell goods at right quantities and time while minimizing the transaction costs. This paper presents our negotiating agent designed for Supply Chain Management League (SCML) in the International Automated Negotiation Agents Competition (ANAC). Basically, the proposed approach relies on determining reservation value by taking the changes in the inventory stock into account. We have tested the performance of our bidding strategy in the competition simulation environment and compared it with the performance of the winner strategies in ANAC SCML 2019. Our experimental results showed that the proposed strategy outperformed the winner strategies in overall.

Cite As: Celal Ozan Berk Yavuz, Çağıl Süslü, and Reyhan Aydoğan, “Taking Inventory Changes into Account While Negotiating in Supply Chain Management”, In Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020), Malta, February 2020.

Type: Conference

Links: https://ieeexplore.ieee.org/document/8925437

Bibtex: @inproceedings{Mell_ACII_2019, author = {Mell, Johnathan and Gratch, Jonathan and Aydou{g}an, Reyhan and Baarslag, Tim and Jonker, Catholijn M.}, booktitle = {2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII)}, title = {The Likeability-Success Tradeoff: Results of the 2nd Annual Human-Agent Automated Negotiating Agents Competition}, year = {2019}, volume = {}, number = {}, pages = {1-7}, keywords = {Task analysis;Bars;Games;Buildings;Robustness;Measurement;Graphical user interfaces;human agent interaction;negotiation;empirical results in HCI}, doi = {10.1109/ACII.2019.8925437} }

Abstract: We present the results of the 2 nd Annual Human-Agent League of the Automated Negotiating Agent Competition. Building on the success of the previous year's results, a new challenge was issued that focused exploring the likeability-success tradeoff in negotiations. By examining a series of repeated negotiations, actions may affect the relationship between automated negotiating agents and their human competitors over time. The results presented herein support a more complex view of human-agent negotiation and capture of integrative potential (win-win solutions). We show that, although likeability is generally seen as a tradeoff to winning, agents are able to remain well-liked while winning if integrative potential is not discovered in a given negotiation. The results indicate that the top-performing agent in this competition took advantage of this loophole by engaging in favor exchange across negotiations (cross-game logrolling). These exploratory results provide information about the effects of different submitted “black-box” agents in human-agent negotiation and provide a state-of-the-art benchmark for human-agent design.

Cite As: Johnathan Mell, Jonathan Gratch, Reyhan Aydoğan, Tim Baarslag and Catholijn M. Jonker, “The Likeability-Success Tradeoff: Results of the 2nd Annual Human-Agent Automated Negotiating Agents Competition”, In Proceedings of the 8th International Conference on Affective Computing and Intelligent Interaction (ACII 2019), Cambridge, UK, September, 2019.

Type: Conference

Links: https://confit.atlas.jp/guide/event/jsai2019/subject/2F1-E-3-01/advanced

Bibtex: @inproceedings{Aydogan_JSAI_2019, title = {ANAC 2018: Repeated Multilateral Negotiation League}, abstract = {This is an extension from a selected paper from JSAI2019. There are a number of research challenges in the field of Automated Negotiation. The Ninth International Automated Negotiating Agent Competition encourages participants to develop effective negotiating agents, which can negotiate with multiple opponents more than once. This paper discusses research challenges for such negotiations as well as presenting the competition set-up and results. The results show that winner agents mostly adopt hybrid bidding strategies that take their opponents' preferences as well as their strategy into account.}, keywords = {Competition, Multi-agent system, Negotiation}, author = {Aydou{g}an, Reyhan and Fujita, Katsuhide and Baarslag, Tim and Jonker, Catholijn and Ito, Takayuki}, note = {Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; The 33rd Annual Conference of the Japanese Society for Artificial Intelligence, JSAI 2019 ; Conference date: 04-06-2019 Through 07-06-2019}, year = {2019}, doi = {10.1007/978-3-030-39878-1_8}, language = {English}, isbn = {9783030398774}, series = {Advances in Intelligent Systems and Computing}, publisher = {Springer}, pages = {77--89}, editor = {Ohsawa, Yukio and Yada, Katsutoshi and Ito, Takayuki and Takama, Yasufumi and Sato-Shimokawara, Eri and Abe, Akinori and Mori, Junichiro and Matsumura, Naohiro}, booktitle = {Advances in Artificial Intelligence - Selected Papers from the Annual Conference of Japanese Society of Artificial Intelligence JSAI 2019}, url = {http://www.ai-gakkai.or.jp/jsai2019/en}, }

Abstract: There are a number of research challenges in the field of Automated Negotiation. The Ninth International Automated Negotiating Agent Competition aimed to encourage participants to develop effective negotiating agents, which can negotiate with multiple opponents more than once. This paper discusses essential research challenges for such negotiations as well as presenting the competition set-up and results. Results showed that winner agents mostly adopt hybrid bidding strategies and take their opponents' preferences as well as their strategy into account.

Cite As: Reyhan Aydoğan, Katsuhide Fujita, Tim Baarslag, Catholijn M. Jonker and Takayuki Ito, “ANAC 2018: Repeated Multilateral Negotiation League” The 33rd Annual Conference of the Japanese Society for Artificial Intelligence, Nigata, Japan, 2019.

Type: Conference

Links: https://link.springer.com/chapter/10.1007/978-3-030-23887-2_6

Bibtex: @inproceedings{Coban_DCIA_2020, author = {c{C}oban, Selami and Sanchez-Anguix, Victor and Aydou{g}an, Reyhan}, editor = {Herrera, Francisco and Matsui, Kenji and Rodr{'i}guez-Gonz{'a}lez, Sara}, title = {Predicting Shuttle Arrival Time in Istanbul}, booktitle = {Distributed Computing and Artificial Intelligence, 16th International Conference}, year = {2020}, publisher = {Springer International Publishing}, address = {Cham}, pages = {44--51}, abstract = {Nowadays, transportation companies look for smart solutions in order to improve quality of their services. Accordingly, an intercity bus company in Istanbul aims to improve their shuttle schedules. This paper proposes revising scheduling of the shuttles based on their estimated travel time in the given timeline. Since travel time varies depending on the date of travel, weather, distance, we present a prediction model using both travel history and additional information such as distance, holiday, and weather. The results showed that Random Forest algorithm outperformed other methods and adding additional features increased its accuracy rate.}, isbn = {978-3-030-23887-2}, }

Abstract: Nowadays, transportation companies look for smart solutions in order to improve quality of their services. Accordingly, an intercity bus company in Istanbul aims to improve their shuttle schedules. This paper proposes revising scheduling of the shuttles based on their estimated travel time in the given timeline. Since travel time varies depending on the date of travel, weather, distance, we present a prediction model using both travel history and additional information such as distance, holiday, and weather. The results showed that Random Forest algorithm outperformed other methods and adding additional features increased its accuracy rate.

Cite As: Selami Çoban, Victor Sanchez-Anguix and Reyhan Aydoğan, “Predicting Shuttle Arrival Time in İstanbul”, Proceedings of Distributed Computing and Artificial Intelligence, 16th International Conference, pp 44-51, Avila, Spain, 2019.

Type: Conference

Links: https://link.springer.com/chapter/10.1007/978-3-030-17294-7_8

Bibtex: @inbook{Jonke_Book_2019, author="de Jonge, Dave and Baarslag, Tim and Aydo{u{g}}an, Reyhan and Jonker, Catholijn and Fujita, Katsuhide and Ito, Takayuki", editor="Lujak, Marin", title="The Challenge of Negotiation in the Game of Diplomacy", booktitle="Agreement Technologies", year="2019", publisher="Springer International Publishing", address="Cham", pages="100--114", abstract="The game of Diplomacy has been used as a test case for complex automated negotiations for a long time, but to date very few successful negotiation algorithms have been implemented for this game. We have therefore decided to include a Diplomacy tournament within the annual Automated Negotiating Agents Competition (ANAC). In this paper we present the setup and the results of the ANAC 2017 Diplomacy Competition and the ANAC 2018 Diplomacy Challenge. We observe that none of the negotiation algorithms submitted to these two editions have been able to significantly improve the performance over a non-negotiating baseline agent. We analyze these algorithms and discuss why it is so hard to write successful negotiation algorithms for Diplomacy. Finally, we provide experimental evidence that, despite these results, coalition formation and coordination do form essential elements of the game.", isbn="978-3-030-17294-7" }

Abstract: This book constitutes the revised selected papers from the 6th International Conference on Agreement Technologies, AT 2018, held in Bergen, Norway, in December 2018. The 11 full papers and 6 short papers presented in this volume were carefully reviewed and selected from a total of 28 submissions. The papers discuss new ideas and techniques for the design, implementation and verification of next generation open distributed systems centered on the notion of agreement among computational agents. They are organized in the following topical sections: AT foundations and modelling of reasoning agents; argumentation and negotiation; coordination in open distributed systems with applications.

Cite As: Dave de Jonge, Tim Baarslag, Reyhan Aydogan, Catholijn Jonker, Katsuhide Fujita and Takayuki Ito, “The Challenge of Negotiation in the Game of Diplomacy”, The Sixth Conference on Agreement Technologies, Bergen – Norway, December 2018.

Type: Conference

Links: https://dl.acm.org/doi/10.1145/3267851.3267907

Bibtex: @inproceedings{Mell_IVA_2018, author = {Mell, Johnathan and Gratch, Jonathan and Baarslag, Tim and Aydou{g}an, Reyhan and Jonker, Catholijn M.}, title = {Results of the First Annual Human-Agent League of the Automated Negotiating Agents Competition}, year = {2018}, isbn = {9781450360135}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3267851.3267907}, doi = {10.1145/3267851.3267907}, abstract = {We present the results of the first annual Human-Agent League of ANAC. By introducing a new human-agent negotiating platform to the research community at large, we facilitated new advancements in human-aware agents. This has succeeded in pushing the envelope in agent design, and creating a corpus of useful human-agent interaction data. Our results indicate a variety of agents were submitted, and that their varying strategies had distinct outcomes on many measures of the negotiation. These agents approach the problems endemic to human negotiation, including user modeling, bidding strategy, rapport techniques, and strategic bargaining. Some agents employed advanced tactics in information gathering or emotional displays and gained more points than their opponents, while others were considered more "likeable" by their partners.}, booktitle = {Proceedings of the 18th International Conference on Intelligent Virtual Agents}, pages = {23--28}, numpages = {6}, keywords = {Human-Agent Negotiation, IAGO Negotiation Platform}, location = {Sydney, NSW, Australia}, series = {IVA '18} }

Abstract: We present the results of the first annual Human-Agent League of ANAC. By introducing a new human-agent negotiating platform to the research community at large, we facilitated new advancements in human-aware agents. This has succeeded in pushing the envelope in agent design, and creating a corpus of useful human-agent interaction data. Our results indicate a variety of agents were submitted, and that their varying strategies had distinct outcomes on many measures of the negotiation. These agents approach the problems endemic to human negotiation, including user modeling, bidding strategy, rapport techniques, and strategic bargaining. Some agents employed advanced tactics in information gathering or emotional displays and gained more points than their opponents, while others were considered more "likeable" by their partners.

Cite As: Johnathan Mell, Jonathan Gratch, Tim Baarslag, Reyhan Aydoğan and Catholijn M. Jonker, "Results of the First Annual Human-Agent League of the Automated Negotiating Agents Competition", IVA 2018: Intelligent Virtual Agents, Australia, November 2018.

Type: Conference

Links: https://link.springer.com/chapter/10.1007/978-3-319-69131-2_31

Bibtex: @inproceedings{Aydogan_PRIMA_2017, author = {Aydou{g}an, Reyhan and "{O}zturk, Pinar and Razeghi, Yousef}, editor = {An, Bo and Bazzan, Ana and Leite, Jo{~a}o and Villata, Serena and van der Torre, Leendert}, title = {Negotiation for Incentive Driven Privacy-Preserving Information Sharing}, booktitle = {PRIMA 2017: Principles and Practice of Multi-Agent Systems}, year = {2017}, publisher = {Springer International Publishing}, address = {Cham}, pages = {486--494}, abstract = {This paper describes an agent-based, incentive-driven, and privacy-preserving information sharing framework. Main contribution of the paper is to give the data provider agent an active role in the information sharing process and to change the currently asymmetric position between the provider and the requester of data and information (DI) to the favor of the DI provider. Instead of a binary yes/no answer to the requester's data request and the incentive offer, the provider may negotiate about excluding from the requested DI bundle certain pieces of DI with high privacy value, and/or ask for a different type of incentive. We show the presented approach on a use case. However, the proposed architecture is domain independent.}, isbn = {978-3-319-69131-2}, }

Abstract: This paper describes an agent-based, incentive-driven, and privacy-preserving information sharing framework. Main contribution of the paper is to give the data provider agent an active role in the information sharing process and to change the currently asymmetric position between the provider and the requester of data and information (DI) to the favor of the DI provider. Instead of a binary yes/no answer to the requester’s data request and the incentive offer, the provider may negotiate about excluding from the requested DI bundle certain pieces of DI with high privacy value, and/or ask for a different type of incentive. We show the presented approach on a use case. However, the proposed architecture is domain independent.

Cite As: Reyhan Aydoğan, Pinar Ozturk, and Yousef Razeghi, “Negotiation for Incentive Driven Privacy-Preserving Information Sharing”, In the Proceedings of PRIMA 2017: Principles and Practice of Multi-Agent Systems, Lecture Notes in Computer Science, vol 10621. Springer, pp. 486-494, 2017.

Type: Conference

Links: https://link.springer.com/chapter/10.1007/978-3-319-69131-2_27

Bibtex: @inproceedings{Gunes_PRIMA_2017, author = {G"{u}nec{s}, Taha D. and Arditi, Emir and Aydou{g}an, Reyhan}, editor = {An, Bo and Bazzan, Ana and Leite, Jo{~a}o and Villata, Serena and van der Torre, Leendert}, title = {Collective Voice of Experts in Multilateral Negotiation}, booktitle = {PRIMA 2017: Principles and Practice of Multi-Agent Systems}, year = {2017}, publisher = {Springer International Publishing}, address = {Cham}, pages = {450--458}, abstract = {Inspired from the ideas such as "algorithm portfolio", "mixture of experts", and "genetic algorithm", this paper presents two novel negotiation strategies, which combine multiple negotiation experts to decide what to bid and what to accept during the negotiation. In the first approach namely incremental portfolio, a bid is constructed by asking each negotiation agent's opinion in the portfolio and picking one of the suggestions stochastically considering the expertise levels of the agents. In the second approach namely crossover strategy, each expert agent makes a bid suggestion and a majority voting is used on each issue value to decide the bid content. The proposed approaches have been evaluated empirically and our experimental results showed that the crossover strategy outperformed the top five finalists of the ANAC 2016 Negotiation Competition in terms of the obtained average individual utility.}, isbn = {978-3-319-69131-2}, }

Abstract: Inspired from the ideas such as “algorithm portfolio”, “mixture of experts”, and “genetic algorithm”, this paper presents two novel negotiation strategies, which combine multiple negotiation experts to decide what to bid and what to accept during the negotiation. In the first approach namely incremental portfolio, a bid is constructed by asking each negotiation agent’s opinion in the portfolio and picking one of the suggestions stochastically considering the expertise levels of the agents. In the second approach namely crossover strategy, each expert agent makes a bid suggestion and a majority voting is used on each issue value to decide the bid content. The proposed approaches have been evaluated empirically and our experimental results showed that the crossover strategy outperformed the top five finalists of the ANAC 2016 Negotiation Competition in terms of the obtained average individual utility.

Cite As: Taha Gunes, Emir Arditi, and Reyhan Aydoğan, “Collective Voice of Experts in Multilateral Negotiation”, In the Proceedings of PRIMA 2017: Principles and Practice of Multi-Agent Systems, Lecture Notes in Computer Science, vol 10621. Springer, pp. 450-458, 2017.

Type: Conference

Links: https://link.springer.com/chapter/10.1007/978-3-319-69131-2_16

Bibtex: @inproceedings{Tunali_PRIMA_2017, author = {Tunal{i}, Okan and Aydou{g}an, Reyhan and Sanchez-Anguix, Victor}, editor = {An, Bo and Bazzan, Ana and Leite, Jo{~a}o and Villata, Serena and van der Torre, Leendert}, title = {Rethinking Frequency Opponent Modeling in Automated Negotiation}, booktitle = {PRIMA 2017: Principles and Practice of Multi-Agent Systems}, year = {2017}, publisher = {Springer International Publishing}, address = {Cham}, pages = {263--279}, abstract = {Frequency opponent modeling is one of the most widely used opponent modeling techniques in automated negotiation, due to its simplicity and its good performance. In fact, it outperforms even more complex mechanisms like Bayesian models. Nevertheless, the classical frequency model does not come without its own assumptions, some of which may not always hold in many realistic settings. This paper advances the state of the art in opponent modeling in automated negotiation by introducing a novel frequency opponent modeling mechanism, which soothes some of the assumptions introduced by classical frequency approaches. The experiments show that our proposed approach outperforms the classic frequency model in terms of evaluation of the outcome space, estimation of the Pareto frontier, and accuracy of both issue value evaluation estimation and issue weight estimation.}, isbn = {978-3-319-69131-2}, }

Abstract: Frequency opponent modeling is one of the most widely used opponent modeling techniques in automated negotiation, due to its simplicity and its good performance. In fact, it outperforms even more complex mechanisms like Bayesian models. Nevertheless, the classical frequency model does not come without its own assumptions, some of which may not always hold in many realistic settings. This paper advances the state of the art in opponent modeling in automated negotiation by introducing a novel frequency opponent modeling mechanism, which soothes some of the assumptions introduced by classical frequency approaches. The experiments show that our proposed approach outperforms the classic frequency model in terms of evaluation of the outcome space, estimation of the Pareto frontier, and accuracy of both issue value evaluation estimation and issue weight estimation.

Cite As: Okan Tunali, Reyhan Aydoğan and Victor Sanchez, “Rethinking Frequency Opponent Modeling in Automated Negotiation”, In the Proceedings of PRIMA 2017: Principles and Practice of Multi-Agent Systems, Lecture Notes in Computer Science, vol 10621. Springer, pp. 263-279, 2017.

Type: Conference

Links: https://www.openconf.org/siu2017/modules/request.php?module=oc_program&action=summary.php&id=700

Bibtex: @inproceedings{Dev_SIU_2017, author = {Dev, Onur Bar{i}c{s} and Aydou{g}an, Reyhan and "{O}ztop, Erhan}, booktitle = {2017 25th Signal Processing and Communications Applications Conference (SIU)}, title = {Yapay yac{s}am sim"{u}lasyonunda hayatta kalmak Surviving in artificial life simulation}, year = {2017}, volume = {}, number = {}, pages = {1-4}, keywords = {Genomics;Bioinformatics;Dogs;Biological system modeling;Computers;Computational modeling;Artificial life;genetic algorithm;multi-agent based simulation}, doi = {10.1109/SIU.2017.7960611}, }

Abstract: By mimicking the mechanics of natural life in the computer environment, artificial life brings out new information about life as well as contributing to the engineering fields. This article introduces a multi-agent based simulation environment in which artificial life studies can be carried out, and presents the preliminary results of our biologically inspired intelligent behaviour synthesis study conducted in this environment.

Cite As: Onur Barış Dev, Reyhan Aydoğan and Erhan Öztop, “Yapay Yaşam Simülasyonunda Hayatta Kalmak”, 25. IEEE Sinyal Isleme ve Iletisim Uygulamalari Kurultayi, Antalya, 2017.

Type: Conference

Links: https://link.springer.com/chapter/10.1007/978-3-319-59294-7_2

Bibtex: @inproceedings{Jonker_PRIMA_2017, author = {Jonker, Catholijn M. and Aydou{g}an, Reyhan and Baarslag, Tim and Broekens, Joost and Detweiler, Christian A. and Hindriks, Koen V. and Huldtgren, Alina and Pasman, Wouter}, editor = {Criado Pacheco, Natalia and Carrascosa, Carlos and Osman, Nardine and Juli{'a}n Inglada, Vicente}, title = {An Introduction to the Pocket Negotiator: A General Purpose Negotiation Support System}, booktitle = {Multi-Agent Systems and Agreement Technologies}, year = {2017}, publisher = {Springer International Publishing}, address = {Cham}, pages = {13--27}, abstract = {The Pocket Negotiator (PN) is a negotiation support system developed at TU Delft as a tool for supporting people in bilateral negotiations over multi-issue negotiation problems in arbitrary domains. Users are supported in setting their preferences, estimating those of their opponent, during the bidding phase and sealing the deal. We describe the overall architecture, the essentials of the underlying techniques, the form that support takes during the negotiation phases, and we share evidence of the effectiveness of the Pocket Negotiator.}, isbn = {978-3-319-59294-7}, }

Abstract: The Pocket Negotiator (PN) is a negotiation support system developed at TU Delft as a tool for supporting people in bilateral negotiations over multi-issue negotiation problems in arbitrary domains. Users are supported in setting their preferences, estimating those of their opponent, during the bidding phase and sealing the deal. We describe the overall architecture, the essentials of the underlying techniques, the form that support takes during the negotiation phases, and we share evidence of the effectiveness of the Pocket Negotiator.

Cite As: Catholijn M. Jonker, Reyhan Aydoğan,Tim Baarslag, Joost Broekens, Christian A. Detweiler, Koen V. Hindriks,Alina Huldtgren and Wouter Pasman, “An Introduction to the Pocket Negotiator: A General Purpose Negotiation Support System“, In Proceedings of Multi-Agent Systems – 14th European Conference, EUMAS 2016, LNAI 10207, Springer International Publishing, pp 13-27, 2017.

Type: Conference

Links: https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/download/14745/14021

Bibtex: @article{Jonker_AAAI_2017, title = {Automated Negotiating Agents Competition (ANAC)}, volume = {31}, url = {https://ojs.aaai.org/index.php/AAAI/article/view/10637}, DOI = {10.1609/aaai.v31i1.10637}, abstractNote = {The annual International Automated Negotiating Agents Competition (ANAC) is used by the automated negotiation research community to benchmark and evaluate its work and to challenge itself. The benchmark problems and evaluation results and the protocols and strategies developed are available to the wider research community.}, number = {1}, journal = {Proceedings of the AAAI Conference on Artificial Intelligence}, author = {Jonker, Catholijn and Aydou{g}an, Reyhan and Baarslag, Tim and Fujita, Katsuhide and Ito, Takayuki and Hindriks, Koen}, year = {2017}, month = {Feb.}, }

Abstract: The annual International Automated Negotiating Agents Competition (ANAC) is used by the automated negotiation research community to benchmark and evaluate its work andto challenge itself. The benchmark problems and evaluation results and the protocols and strategies developed are available to the wider research community.

Cite As: Catholijn M. Jonker, Reyhan Aydoğan,Tim Baarslag,Katsuhide Fujita, Takayuki It, and Koen Hindriks, "Automated Negotiating Agents Competition (ANAC)", In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-2017), pp. 5070 -5072, AAAI Press, 2017.

Type: Conference

Links: https://ii.tudelft.nl/?q=node/8168

Bibtex: @inproceedings{Baarslag_WI-IAT_2015, author = {Baarslag, Tim and Gerding, Enrico H. and Aydou{g}an, Reyhan and Schraefel, M. C.}, booktitle = {2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)}, title = {Optimal Negotiation Decision Functions in Time-Sensitive Domains}, year = {2015}, volume = {2}, number = {}, pages = {190-197}, keywords = {Uncertainty;Electronic mail;Planning;Force;Cost function;Games;Intelligent agents;Bidding strategy;Concessions;Decision function;Simultaneous search;Cost;Costly negotiation;Time-sensitive;Cascade model;Negotiation;Automated negotiation;Distributive bargaining;Integrative bargaining;Optimal offers}, doi = {10.1109/WI-IAT.2015.161}, }

Abstract: The last two decades have seen a growing interest in automated agents that are able to negotiate on behalf of human negotiators in a wide variety of negotiation domains. One key aspect of a successful negotiating agent is its ability to make appropriate concessions at the right time, especially when there are costs associated with the duration of the negotiation. However, so far, there is no fundamental approach on how much to concede at every stage of the negotiation in such time-sensitive domains. We introduce an efficient solution based on simultaneous search, which is able to select the optimal sequence of offers that maximizes expected payoff, given the agent's beliefs about the opponent. To this end, we show that our approach is consistent with known theoretical results and we demonstrate both its effectiveness and natural properties by applying it to a number of typical negotiation scenarios. Finally, we show in a number of experiments that our solution outperforms other state of the art strategy benchmarks.

Cite As: Tim Baarslag, Enrico Gerding, Reyhan Aydoğan, and M.c. Schrael, "Optimal Negotiation Decision Functions in Time-Sensitive Domains”, In Proceedings of the 2015 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2015.

Type: Conference

Links: https://link.springer.com/chapter/10.1007/978-3-319-17130-2_2

Bibtex: @InProceedings{Aydogan_MAS_2015, author = {Aydou{g}an, Reyhan and Sharpanskykh, Alexei and Lo, Julia}, editor = {Bulling, Nils}, title = {A Trust-Based Situation Awareness Model}, booktitle = {Multi-Agent Systems}, year = {2015}, publisher = {Springer International Publishing}, address = {Cham}, pages = {19--34}, abstract = {Trust is a social phenomenon that impacts the situation awareness of individuals and indirectly their decision-making. However, most of the existing computational models of situation awareness do not take interpersonal trust into account. Contrary to those models, this study introduces a computational, agent-based situation awareness model incorporating trust to enable building more human-like decision making tools. To illustrate the proposed model, a simulation case study has been conducted in the airline operation control domain. According to the results of this study, the trustworthiness of information sources had a significant effect on airline operation controller's situation awareness.}, isbn = {978-3-319-17130-2} }

Abstract: Trust is a social phenomenon that impacts the situation awareness of individuals and indirectly their decision-making. However, most of the existing computational models of situation awareness do not take interpersonal trust into account. Contrary to those models, this study introduces a computational, agent-based situation awareness model incorporating trust to enable building more human-like decision making tools. To illustrate the proposed model, a simulation case study has been conducted in the airline operation control domain. According to the results of this study, the trustworthiness of information sources had a significant effect on airline operation controller’s situation awareness.

Cite As: Reyhan Aydoğan, Alexei Sharpanskykh, and Julia Lo, “A Trust-based Situation Awareness Model”, In Proceedings of the 12th European Conference on Multi-Agent Systems (EUMAS - 2014), December, Prague, 2014.

Type: Conference

Links: http://dl.acm.org/citation.cfm?id=2615799

Bibtex: @inproceedings{Harbers_AAMAS_2014, author = {Harbers, Maaike and Aydou{g}an, Reyhan and Jonker, Catholijn M. and Neerincx, Mark A.}, title = {Sharing information in teams: giving up privacy or compromising on team performance?}, year = {2014}, isbn = {9781450327381}, publisher = {International Foundation for Autonomous Agents and Multiagent Systems}, address = {Richland, SC}, abstract = {Human teamwork can be supported by agent technology by providing each human team member with an agent that monitors, supports and advices the human. The agent can, for example, monitor the human's workload, and share that information with (agents of) other team members so that work can be distributed effectively. However, though sharing information can lead to a higher team performance, it may violate the individual team members' privacy. This raises the question what type of and how often information should be shared between team members. This paper addresses this question by studying the trade-off between privacy loss and team performance in the train traffic control domain. We provide a conceptual domain analysis, introduce a formal model of train traffic control teams and their dynamics, and describe an agent-based simulation experiment that investigates the effects of sharing different types and amounts of information on privacy loss and team performance. The results give insight in the extent to which different information types cause privacy loss and contribute to team performance. This work enables the design of privacy-sensitive support agents for teamwork.}, booktitle = {Proceedings of the 2014 International Conference on Autonomous Agents and Multi-Agent Systems}, pages = {413--420}, numpages = {8}, keywords = {teamwork, team performance, privacy, information sharing, agent-based modeling}, location = {Paris, France}, series = {AAMAS '14} }

Abstract: Human teamwork can be supported by agent technology by providing each human team member with an agent that monitors, supports and advices the human. The agent can, for example, monitor the human's workload, and share that information with (agents of) other team members so that work can be distributed effectively. However, though sharing information can lead to a higher team performance, it may violate the individual team members' privacy. This raises the question what type of and how often information should be shared between team members. This paper addresses this question by studying the trade-off between privacy loss and team performance in the train traffic control domain. We provide a conceptual domain analysis, introduce a formal model of train traffic control teams and their dynamics, and describe an agent-based simulation experiment that investigates the effects of sharing different types and amounts of information on privacy loss and team performance. The results give insight in the extent to which different information types cause privacy loss and contribute to team performance. This work enables the design of privacy-sensitive support agents for teamwork.

Cite As: Maaike Harbers, Reyhan Aydoğan, Catholijn M. Jonker and Mark A. Neerincx, "Sharing information in teams: giving up privacy or compromising on team performance?" In Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2014), pp. 413-420, Paris, France, May 5-9, 2014.

Type: Conference

Links: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6928213

Bibtex: @INPROCEEDINGS{Oey_WI_IAT_2014, author = {Oey, Michel and Genc{c}, Z"{u}lk"{u}f and Ghorbani, Amineh and Aldewereld, Huib and Brazier, Frances and Aydou{g}an, Reyhan and Jonker, Catholijn M. and Timmer, Reinier and Wijngaards, Niek}, booktitle = {2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)}, title = {Modelling Multi-stakeholder Systems: A Case Study}, year = {2014}, volume = {3}, number = {}, pages = {404-411}, keywords = {simulation;modelling;multi-agent systems;energy domain;negotiations;multi-stakeholder;horizontal governance}, doi = {10.1109/WI-IAT.2014.195} }

Abstract: A contemporary governance challenge for governments concerns the biogas domain: what incentives and policies can lead to a viable biogas economy? To support addressing this challenge, a prototype of a simulator is constructed in which horizontal governance is applied in a multi-stakeholder context. This paper reports on the modelling and knowledge acquisition that led to the development of that prototype. Rather than (re)inventing tooling, three available agent-based modelling approaches are combined: the MAIA meta-model, OperA and GENIUS, with Agents cape as the agent-based middleware for the realisation of the simulator. The resulting simulator has been validated by biogas experts from Alliander (NL-based energy network company), leading to confirmation that our combined approach was useful for the analysis of this multi-stakeholder domain.

Cite As: Michel Oey, Zülküf Genç, Amineh Ghorbani, Huib Aldewereld, Reyhan Aydogan, Niek Wijngaards, Reinier Timmer, Frances Brazier, Catholijn Jonker, “Modelling Multi-Stakeholder Systems: A Case Study”, In Proceedings of the 2014 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'14), Volume: Special session on Agent-Based Simulation Supporting Decision Makin, pp. 404-411, Warsaw, Poland, 2014.

Type: Conference

Links: http://link.springer.com/chapter/10.1007%2F978-3-319-04954-0_22

Bibtex: @InProceedings{Aydogan_FGS_2014, author = {Aydou{g}an, Reyhan and Lo, Julia C. and Meijer, Sebastiaan A. and Jonker, Catholijn M.}, editor = {Meijer, Sebastiaan A. and Smeds, Riitta}, title = {Modeling Network Controller Decisions Based Upon Situation Awareness through Agent-Based Negotiation}, booktitle = {Frontiers in Gaming Simulation}, year = {2014}, publisher = {Springer International Publishing}, address = {Cham}, pages = {191--200}, abstract = {The Dutch railway traffic control is in an urgent need for innovation and therefore turns to gaming simulation as a platform to test and train future configurations of the system. The presence of relevant participants is necessary to keep the fidelity of the gaming simulation high. Network controllers are often needed in such games, but are expensive, scarce, and often have limited action, thus making their involvement less than desirable. To overcome this, the current paper introduces the use of intelligent software agents to replace some roles. The cognitive construct of situation awareness is required to model the evaluation of an offer in a negotiation setting, in which a situation awareness model (SAM) is introduced for evaluating offers in complex and dynamic systems.}, isbn = {978-3-319-04954-0} }

Abstract: The Dutch railway traffic control is in an urgent need for innovation and therefore turns to gaming simulation as a platform to test and train future configurations of the system. The presence of relevant participants is necessary to keep the fidelity of the gaming simulation high. Network controllers are often needed in such games, but are expensive, scarce, and often have limited action, thus making their involvement less than desirable. To overcome this, the current paper introduces the use of intelligent software agents to replace some roles. The cognitive construct of situation awareness is required to model the evaluation of an offer in a negotiation setting, in which a situation awareness model (SAM) is introduced for evaluating offers in complex and dynamic systems.

Cite As: Reyhan Aydoğan, Julia C. Lo, Sebastiaan A. Meijer and Catholijn M. Jonker, "Modeling Network Controller Decisions Based Upon Situation Awareness Through Agent-based Negotiation Systems", In Proceedings of the 44th Annual, International Conference of the International Simulation and Gaming Association, Stockholm, 2013.

Type: Conference

Links: http://dx.doi.org/10.5220/0003718302430248

Bibtex: @conference{Visser_ICAART_2012, author = {Visser, Wietske and Aydou{g}an, Reyhan and Hindriks, Koen V. and Jonker, Catholijn M.}, title = {A FRAMEWORK FOR QUALITATIVE MULTI-CRITERIA PREFERENCES}, booktitle = {Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART}, year = {2012}, pages = {243-248}, publisher = {SciTePress}, organization = {INSTICC}, doi = {10.5220/0003718302430248}, isbn = {978-989-8425-95-9}, issn = {2184-433X}, }

Abstract: A key challenge in the representation of qualitative, multi-criteria preferences is to find a compact and expressive representation. Various frameworks have been introduced, each of which with its own distinguishing features. In this paper we introduce a new representation framework called qualitative preference systems (QPS), which combines priority, cardinality and conditional preferences. Moreover, the framework incorporates knowledge that serves two purposes: to impose (hard) constraints, but also to define new (abstract) concepts. In short, QPS offers a rich and practical representation for qualitative, multi-criteria preferences.

Cite As: Wietske Visser, Reyhan Aydoğan, Koen Hindriks, and Catholijn Jonker, “A Framework for Qualitative Multi-Criteria Preferences”, In Proceedings of the Forth International Conference on Agents and Artificial Intelligence (ICAART 2012), pp. 243–248, Portugal, 2012.

Type: Conference

Links: https://dl.acm.org/doi/10.5555/1838206.1838503

Bibtex: @inproceedings{Aydogan_AAMAS_2010, author = {Aydou{g}an, Reyhan and Yolum, Pinar}, title = {Effective negotiation with partial preference information}, year = {2010}, isbn = {9780982657119}, publisher = {International Foundation for Autonomous Agents and Multiagent Systems}, address = {Richland, SC}, abstract = {Users' preferences play a key role in automated negotiation since they dictate how an agent will act on behalf of its user. However, elicitation of these preferences from the user is difficul when there are dependencies between preferences. In many settings, expecting a user to provide a total ordering of her preferences is unrealistic. Thus, it is essential to build agents that can negotiate with only partial preference information. In order to achieve this goal, we develop negotiation strategies that work on qualitative preference representations, such as CP-nets that require only partial preference information.}, booktitle = {Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: Volume 1 - Volume 1}, pages = {1605--1606}, numpages = {2}, keywords = {testing of agent systems, preference, negotiation}, location = {Toronto, Canada}, series = {AAMAS '10} }

Abstract: Users' preferences play a key role in automated negotiation since they dictate how an agent will act on behalf of its user. However, elicitation of these preferences from the user is difficul when there are dependencies between preferences. In many settings, expecting a user to provide a total ordering of her preferences is unrealistic. Thus, it is essential to build agents that can negotiate with only partial preference information. In order to achieve this goal, we develop negotiation strategies that work on qualitative preference representations, such as CP-nets that require only partial preference information.

Cite As: Reyhan Aydoğan and Pınar Yolum, “Effective Negotiation with Partial Preference Information”, In Proceedings of the Ninth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 1605-1606, Toronto, Canada, 2010.

Type: Conference

Links: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5284842

Bibtex: @INPROCEEDINGS{Aydogan_WI_IAT_2009, author = {Aydou{g}an, Reyhan and Yolum, Pinar}, booktitle = {2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology}, title = {Ontology-Based Learning for Negotiation}, year = {2009}, volume = {2}, number = {}, pages = {177-184}, keywords = {Ontologies;Intelligent agent;Conferences;Counting circuits;Feedback;Negotiation;Preference Learning;Ontology Reasoning}, doi = {10.1109/WI-IAT.2009.148} }

Abstract: Successful negotiation depends on understanding and responding to participants' needs. Many negotiation approaches assume identical needs and do not take into account other preferences of the participants. However, preferences play a crucial role in the outcome of negotiations. Accordingly, we propose a learning algorithm that can be used by a producer during negotiation to understand consumer's needs and to offer services that respect these preferences. Our proposed algorithm is based on inductive learning but also incorporates the idea of revision. Thus, as the negotiation proceeds, a producer can revise its idea of the customer's preferences. The learning is enhanced with the use of ontologies so that similar service requests can be identified and treated similarly. Further, the algorithm is targeted to learning both conjunctive as well as disjunctive preferences. Hence, even if the consumer's preferences are specified in complex ways, such as conditional rules, our algorithm can learn and guide the producer to create well-targeted offers.

Cite As: Reyhan Aydoğan and Pınar Yolum, “Ontology-Based Learning for Negotiation”, In Proceedings of IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT), pp. 177-184, Milan, Italy, 2009.

Type: Conference

Links: http://www.ifaamas.org/Proceedings/.../C_SP_0204.pdf

Bibtex: @inproceedings{Aydogan_AAMAS_2009, author = {Aydoundefinedan, Reyhan and Yolum, Pinar}, title = {Learning disjunctive preferences for negotiating effectively}, year = {2009}, isbn = {9780981738178}, publisher = {International Foundation for Autonomous Agents and Multiagent Systems}, address = {Richland, SC}, abstract = {Successful negotiation depends on understanding and responding to participants' needs. Many negotiation approaches assume identical needs (e.g., minimizing costs) and do not take into account other preferences of the participants. However, preferences play a crucial role in the outcome of negotiations. Accordingly, we propose a negotiation framework where producer agents learn the preferences of consumer preferences over time and negotiates based on this new knowledge. Our proposed approach is based on inductive learning but also incorporates the idea of revision. Thus, as the negotiation proceeds, a producer can revise its idea of the customer's preferences. This enables us to learn conjunctive as well as disjunctive preferences. Even if the consumer's preferences are specified in complex ways, such as conditional rules, our approach can learn and guide the producer to create well-targeted offers. Our experimental work shows that our proposed approach completes negotiation faster than similar approaches, especially if the producer will not be able to satisfy consumer's requests properly.}, booktitle = {Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2}, pages = {1201-1202}, numpages = {2}, keywords = {semantic similarity, ontology, negotiation, inductive learning}, location = {Budapest, Hungary}, series = {AAMAS '09} }

Abstract: Successful negotiation depends on understanding and responding to participants’ needs. Many negotiation approaches assume identical needs (e.g., minimizing costs) and do not take into account other preferences of the participants. However, preferences play a crucial role in the outcome of negotiations. Accordingly, we propose a negotiation framework where producer agents learn the preferences of consumer preferences over time and negotiates based on this new knowledge. Our proposed approach is based on inductive learning but also incorporates the idea of revision. Thus, as the negotiation proceeds, a producer can revise its idea of the customer’s preferences. This enables us to learn conjunctive as well as disjunctive preferences. Even if the consumer’s preferences are specified in complex ways, such as conditional rules, our approach can learn and guide the producer to create well-targeted offers. Our experimental work shows that our proposed approach completes negotiation faster than similar approaches, especially if the producer will not be able to satisfy consumer’s requests properly.

Cite As: Reyhan Aydoğan and Pınar Yolum, “Learning Disjunctive Preferences for Negotiating Effectively”, In Proceedings of the Eighth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 1201-1202, Budapest, Hungary, 2009.

Type: Conference

Links: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4279724&tag=1

Bibtex: @INPROCEEDINGS{Aydogan_ICWS_2007, author = {Aydou{g}an, Reyhan and Zirtiloglu, Hande}, booktitle = {IEEE International Conference on Web Services (ICWS 2007)}, title = {A Graph-Based Web Service Composition Technique Using Ontological Information}, year = {2007}, volume = {}, number = {}, pages = {1154-1155}, keywords = {Ontologies;Web services;Service oriented architecture}, doi = {10.1109/ICWS.2007.6} }

Abstract: We investigate Web service composition as a planning problem and use the input-output parameter relations in order to select the constituent services that make up the composite service. Furthermore, we make use of ontological information between the input-output parameters such that a more specific concept can be used instead of a general concept to make the process more flexible. Our proposed approach is based on constructing a dependency graph including the service parameters and Web services themselves. By using this dependency graph, we perform backward chaining starting to search from the desired output parameters, which is in fact the goal, to the available input parameters. In addition to using semantic information through the search, our approach considers non-functional attributes of the services such as service quality. Considering the quality measures, we find the constituent services by making use of depth first search. After finding the required services, our algorithm generates a plan that shows the execution order of each service.

Cite As: Reyhan Aydoğan and Hande Zırtıloğlu, “A Graph-Based Web Service Composition Technique Using Ontological Information”, In Proceedings of IEEE International Conference on Web Services (ICWS), pp. 1154-1155, 2007.

Type: Conference

Links: http://desktopson/Desktop/OZUEvaluation2018/dl.acm.org/ft_gateway.cfm?id=1329401

Bibtex: @inproceedings{Aydogan_AAMAS_2007, author = {Aydou{g}an, Reyhan and Yolum, Pinar}, title = {Learning consumer preferences using semantic similarity}, year = {2007}, isbn = {9788190426275}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/1329125.1329401}, doi = {10.1145/1329125.1329401}, abstract = {In online, dynamic environments, the services requested by consumers may not be readily served by the providers. This requires the service consumers and providers to negotiate their service needs and offers. Multiagent negotiation approaches typically assume that the parties agree on service content and focus on finding a consensus on service price. In contrast, this work develops an approach through which the parties can negotiate the content of a service. This calls for a negotiation approach in which the parties can understand the semantics of their requests and offers and learn each other's preferences incrementally over time. Accordingly, we propose an architecture in which both consumers and producers use a shared ontology to negotiate a service. Through repetitive interactions, the provider learns consumers' needs accurately and can make better targeted offers. To enable fast and accurate learning of preferences, we develop an extension to Version Space and compare it with existing learning techniques. We further develop a metric for measuring semantic similarity between services and compare the performance of our approach using different similarity metrics.}, booktitle = {Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems}, articleno = {229}, numpages = {8}, keywords = {semantic similarity, ontology, negotiation, inductive learning}, location = {Honolulu, Hawaii}, series = {AAMAS '07} }

Abstract: In online, dynamic environments, the services requested by consumers may not be readily served by the providers. This requires the service consumers and providers to negotiate their service needs and offers. Multiagent negotiation approaches typically assume that the parties agree on service content and focus on finding a consensus on service price. In contrast, this work develops an approach through which the parties can negotiate the content of a service. This calls for a negotiation approach in which the parties can understand the semantics of their requests and offers and learn each other’s preferences incrementally over time. Accordingly, we propose an architecture in which both consumers and producers use a shared ontology to negotiate a service. Through repetitive interactions, the provider learns consumers’ needs accurately and can make better targeted offers. To enable fast and accurate learning of preferences, we develop an extension to Version Space and compare it with existing learning techniques. We further develop a metric for measuring semantic similarity between services and compare the performance of our approach using different similarity metrics.

Cite As: Reyhan Aydoğan and Pınar Yolum, “Learning Consumer Preferences Using Semantic Similarity”, In Proceedings of the Sixth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 1293-1300, Honolulu, Hawaii, May, 2007.

Type: Conference

Links: https://link.springer.com/book/10.1007/978-3-031-40878-6

Bibtex: @proceedings{Calvaresi_Book_2023, title= {Explainable and Transparent AI and Multi-Agent Systems: 5th International Workshop, EXTRAAMAS 2023, London, UK, May 29, 2023, Revised Selected Papers}, year= {2023}, isbn= {978-3-031-40877-9}, publisher= {Springer-Verlag}, address= {Berlin, Heidelberg}, location= {London, United Kingdom} }

Abstract: This volume LNCS 14127 constitutes the refereed proceedings of the 5th International Workshop, EXTRAAMAS 2023, held in London, UK, in May 2023. The 15 full papers presented together with 1 short paper were carefully reviewed and selected from 26 submissions. The workshop focuses on Explainable Agents and multi-agent systems; Explainable Machine Learning; and Cross-domain applied XAI.

Cite As: Davide Calvaresi, Amro Najjar, Andrea Omicini, Reyhan Aydogan, Rachele Carli, Giovanni Ciatto, Yazan Mualla, and Kary Kramling, "Explainable and Transparent AI and Multi-Agent Systems: 5th International Workshop, EXTRAAMAS 2023", Springer, 2023

Type: Book

Links: https://link.springer.com/book/10.1007/978-981-99-0561-4

Bibtex: @book{Hadfi_Book_2023, place= {Singapore}, title= {Recent advances in agent-based negotiation: Applications and competition challenges}, publisher= {Springer}, author= {Hadfi, Rafik and Aydou{g}an, Reyhan and Ito, Takayuki and Arisaka, Ryuta}, year= {2023} }

Abstract: This book comprises carefully selected and reviewed outcomes of the 13th International Workshop on Automated Negotiations (ACAN) held in Vienna, 2022, in conjunction with International Joint Conference on Artificial Intelligence (IJCAI) 2022. It focuses on the applications and challenges of agent-based negotiation including agreement technology, mechanism design, electronic commerce, recommender systems, supply chain management, social choice theory, and others. This book is intended for the academic and industrial researchers of various communities of autonomous agents and multi-agent systems, as well as graduate students studying in those areas or having interest in them.

Cite As: Rafik Hadfi, Reyhan Aydoğan, Takayuki Ito, and Ryuta Arisaka, "Recent Advances in Agent-Based Negotiation: Applications and Competition Challenges", Springer 2023

Type: Book

Links: https://link.springer.com/book/10.1007/978-981-16-0471-3

Bibtex: @book{Aydogan_Book_2021, author= {Aydou{g}an, Reyhan and Ito, Takayuki and Moustafa, Ahmed and Otsuka, Takanobu and Zhang, Minjie}, year= {2021}, month= {01}, pages= {}, publisher={Springer}, title= {Recent Advances in Agent-based Negotiation Formal Models and Human Aspects: Formal Models and Human Aspects}, isbn= {978-981-16-0470-6}, doi= {10.1007/978-981-16-0471-3} }

Abstract: This volume comprises carefully selected and reviewed outcomes of the 12th International Workshop on Automated Negotiations (ACAN) held in Macao, 2019, in conjunction with International Joint Conference on Artificial Intelligence (IJCAI) 2019. It focuses on human aspects of automated negotiation and the recent advances in negotiation frameworks and strategies. Written by leading academic and industrial researchers, it is a valuable resource for professionals and scholars working on complex automated negotiations.

Cite As: Reyhan Aydoğan, Takayuki Ito, Ahmed Moustafa, Takanobu Otsuka, and Minjie Zhang, "Recent Advances in Agent-based Negotiation", Springer 2021

Type: Book

Links: https://link.springer.com/book/10.1007%2F978-981-15-5869-6

Bibtex: @book{Ito_Book_Advances_2021, author= {Ito, Takayuki and Zhang, Minjie and Aydou{g}an, Reyhan}, year= {2021}, month= {01}, publisher={Springer}, pages= {}, title= {Advances in Automated Negotiations}, isbn= {978-981-15-5868-9}, doi= {10.1007/978-981-15-5869-6} }

Abstract: This book discusses important recent advances in automated negotiations. It introduces a number of state-of-the-art autonomous agents for large-scale and complex negotiations, and demonstrates that automated negotiation is one of the most important areas in the field of autonomous agents and multi-agent systems. Further, it presents automated negotiation scenarios involving negotiation encounters that may have, for instance, a large number of agents or a large number of issues with interdependencies and/or real-time constraints. This book includes carefully selected and reviewed outcomes of the 11th International Workshop on Automated Negotiations (ACAN) held in Stockholm, Sweden, 2018, in conjunction with IJCAI-ECAI-2018. Written by leading academic and industrial researchers, it is a valuable resource for professionals and scholars working on complex automated negotiations. Furthermore, the in-depth descriptions of automated negotiating agent programs help readers who are involved in writing codes for automated agents.

Cite As: Takayuki Ito, Minjie Zhang, and Reyhan Aydoğan, "Advances in Automated Negotiations", ACAN, Studies in Computational Intelligence, Volume 905, Springer, 2021

Type: Book

Links: https://link.springer.com/article/10.1007/s10726-021-09738-x

Bibtex: @article{Aydogan_Book_Conflict_2021, author= {Aydou{g}an, Reyhan and Baarslag, Tim and Gerding, Enrico}, year= {2021}, month= {05}, pages= {}, publisher={Springer}, title= {Artificial Intelligence Techniques for Conflict Resolution}, volume= {30}, journal= {Group Decision and Negotiation}, doi= {10.1007/s10726-021-09738-x} }

Abstract: Conflict resolution is essential to obtain cooperation in many scenarios such as politics and business, as well as our day to day life. The importance of conflict resolution has driven research in many fields like anthropology, social science, psychology, mathematics, biology and, more recently, in artificial intelligence. Computer science and artificial intelligence have, in turn, been inspired by theories and techniques from these disciplines, which has led to a variety of computational models and approaches, such as automated negotiation, group decision making, argumentation, preference aggregation, and human-machine interaction. To bring together the different research strands and disciplines in conflict resolution, the Workshop on Conflict Resolution in Decision Making (COREDEMA) was organized. This special issue benefited from the workshop series, and consists of significantly extended and revised selected papers from the ECAI 2016 COREDEMA workshop, as well as completely new contributions.

Cite As: Reyhan Aydoğan,Tim Baarslag and Enrico Gerding, "Artificial Intelligence Techniques for Conflict Resolution", GDN, 30, pp. 879–883, 2021

Type: Book

Links: https://link.springer.com/book/10.1007/978-981-16-0471-3

Bibtex: @book{Aydogan_Book_Advances_2021, author= {Aydou{g}an, Reyhan and Ito, Takayuki and Moustafa, Ahmed and Otsuka, Takanobu and Zhang, Minjie}, year= {2021}, month= {01}, pages= {}, publisher={Springer}, title= {Recent Advances in Agent-based Negotiation Formal Models and Human Aspects: Formal Models and Human Aspects}, isbn= {978-981-16-0470-6}, doi= {10.1007/978-981-16-0471-3} }

Abstract: This volume comprises carefully selected and reviewed outcomes of the 12th International Workshop on Automated Negotiations (ACAN) held in Macao, 2019, in conjunction with International Joint Conference on Artificial Intelligence (IJCAI) 2019. It focuses on human aspects of automated negotiation and the recent advances in negotiation frameworks and strategies. Written by leading academic and industrial researchers, it is a valuable resource for professionals and scholars working on complex automated negotiations.

Cite As: Reyhan Aydoğan, Takayuki Ito, Ahmed Moustafa, Takanobu Otsuka, and Minjie Zhang, "Recent Advances in Agent-based Negotiation", Springer 2021

Type: Book

Links: https://link.springer.com/book/10.1007%2F978-981-15-5869-6

Bibtex: @book{Ito_Book_Automated_2021, author= {Ito, Takayuki and Zhang, Minjie and Aydou{g}an, Reyhan}, year= {2021}, month= {01}, pages= {}, publisher={Springer}, title= {Advances in Automated Negotiations}, isbn= {978-981-15-5868-9}, doi= {10.1007/978-981-15-5869-6} }

Abstract: This book discusses important recent advances in automated negotiations. It introduces a number of state-of-the-art autonomous agents for large-scale and complex negotiations, and demonstrates that automated negotiation is one of the most important areas in the field of autonomous agents and multi-agent systems. Further, it presents automated negotiation scenarios involving negotiation encounters that may have, for instance, a large number of agents or a large number of issues with interdependencies and/or real-time constraints. This book includes carefully selected and reviewed outcomes of the 11th International Workshop on Automated Negotiations (ACAN) held in Stockholm, Sweden, 2018, in conjunction with IJCAI-ECAI-2018. Written by leading academic and industrial researchers, it is a valuable resource for professionals and scholars working on complex automated negotiations. Furthermore, the in-depth descriptions of automated negotiating agent programs help readers who are involved in writing codes for automated agents.

Cite As: Takayuki Ito, Minjie Zhang, and Reyhan Aydoğan, "Advances in Automated Negotiations", ACAN, Studies in Computational Intelligence, Volume 905, Springer, 2020

Type: Book

Links: https://www.springer.com/gp/book/9783319572840

Bibtex: @book{Aydogan_Book_2017, author= {Aydou{g}an, Reyhan and Baarslag, Tim and Gerding, Enrico and Jonker, Catholijn and Juli{'a}n, Vicente and Sanchez-Anguix, V{'i}ctor}, year= {2017}, month= {01}, pages= {}, publisher={Springer}, title= {Conflict Resolution in Decision Making: Second International Workshop, COREDEMA 2016, The Hague, The Netherlands, August 29-30, 2016, Revised Selected Papers}, isbn= {978-3-319-57284-0}, doi= {10.1007/978-3-319-57285-7} }

Abstract: This book constitutes thoroughly revised selected papers of the Second International Workshop on Conflict and Resolution in Decision Makrung, COREDEMA 2016, held in The Hague, The Netherlands, in August 2016. The 9 revised papers presented were carefully reviewed and selected from 13 submissions. The 2nd International Workshop on Conflict Resolution in Decision Making (COREDEMA 2016) focuses on theoretical and practical computational approaches for solving and understanding conflict resolution.

Cite As: Aydoğan R., Baarslag T., Gerding E., Jonker C., Julian V., Sanchez-Anguix V. (eds), "Conflict Resolution in Decision Making", Springer, 2017

Type: Book

Links: https://www.springer.com/gp/book/9783319515618

Bibtex: @book{Fujita_Book_2017, place= {Cham, Switzerland}, title= {Modern approaches to agent-based complex automated negotiation}, publisher= {Springer}, author= {Fujita, Katsuhide and Bai, Quan and Ito, Takayuki and Zhang, Minjie and Ren, Fenghui and Aydou{g}an, Reyhan and Hadfi, Rafik}, year= {2017} }

Abstract: This book addresses several important aspects of complex automated negotiations and introduces a number of modern approaches for facilitating agents to conduct complex negotiations. It demonstrates that autonomous negotiation is one of the most important areas in the field of autonomous agents and multi-agent systems. Further, it presents complex automated negotiation scenarios that involve negotiation encounters that may have, for instance, a large number of agents, a large number of issues with strong interdependencies and/or real-time constraints.

Cite As: Fujita, K, Bai, Q, Ito, T, Zhang, M, Ren, F, Aydoğan, R, & Hadfi, R(Eds.), "Modern Approaches to Agent-based Complex Automated Negotiation", Springer, 2017

Type: Book

Links: http://www.tandfonline.com/doi/abs/10.1080/01969722.2014.894844#.VTDyAOHFiK5

Bibtex: @article{Aydogan_Book_2014, author= {Aydou{g}an, Reyhan and Sanchez, Victor and Julian, Vicente and Broekens, Joost and Jonker, Catholijn}, title= {GUEST EDITORIAL: COMPUTATIONAL APPROACHES FOR CONFLICT RESOLUTION IN DECISION MAKING: NEW ADVANCES AND DEVELOPMENTS}, journal= {Cybernetics and Systems}, volume= {45}, number= {3}, pages= {217--221}, year= {2014}, publisher= {Taylor & Francis}, doi= {10.1080/01969722.2014.894844}, url= {https://doi.org/10.1080/01969722.2014.894844}, eprint= {https://doi.org/10.1080/01969722.2014.894844} }

Abstract: Conflict is an omnipresent phenomenon in human society. It spans from individual decision-making trade-offs such as deciding what to do next (sleep, eat, work, play), to complex scenarios including politics and business. The social sciences, psychology, economy, and biology study the nature of conflict, its consequences, and strategies to successfully deal with it. Over the last decades computer science has joined those disciplines and studies conflict from a computational perspective. This special issue presents a selection of the best papers presented at the First Workshop of Conflict Resolution in Decision Making (COREDEMA). The workshop focused on computational approaches that tackle conflict in order to provide new insights and explore potential applications. The workshop was jointly hosted with the 12th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS) in Salamanca, Spain, from June 4 to 6, 2013.

Cite As: Reyhan Aydoğan,Victor Sanchez-Anguix, Vicente Julian, Joost Broekens and Catholijn M. Jonker, “Computational Approaches for Conflict Resolution in Decision Making: New Advances and Developments”, Cybernetics and Systems, 45:3, pp. 217-221, 2014 (guest editor)

Type: Book

Links: https://link.springer.com/chapter/10.1007/978-3-031-40878-6_12

Bibtex: @InProceedings{Hulstijn_ETAMAS_2023, author={Hulstijn, Joris and Tchappi, Igor and Najjar, Amro and Aydo{u{g}}an, Reyhan}, editor={Calvaresi, Davide and Najjar, Amro and Omicini, Andrea and Aydogan, Reyhan and Carli, Rachele and Ciatto, Giovanni and Mualla, Yazan and Fr{"a}mling, Kary}, title={Metrics for Evaluating Explainable Recommender Systems}, booktitle={Explainable and Transparent AI and Multi-Agent Systems}, year={2023}, publisher={Springer Nature Switzerland}, address={Cham}, pages={212--230}, abstract={Recommender systems aim to support their users by reducing information overload so that they can make better decisions. Recommender systems must be transparent, so users can form mental models about the system's goals, internal state, and capabilities, that are in line with their actual design. Explanations and transparent behaviour of the system should inspire trust and, ultimately, lead to more persuasive recommendations. Here, explanations convey reasons why a recommendation is given or how the system forms its recommendations. This paper focuses on the question how such claims about effectiveness of explanations can be evaluated. Accordingly, we investigate various models that are used to assess the effects of explanations and recommendations. We discuss objective and subjective measurement and argue that both are needed. We define a set of metrics for measuring the effectiveness of explanations and recommendations. The feasibility of using these metrics is discussed in the context of a specific explainable recommender system in the food and health domain.}, isbn={978-3-031-40878-6} }

Abstract: Recommender systems aim to support their users by reducing information overload so that they can make better decisions. Recommender systems must be transparent, so users can form mental models about the system’s goals, internal state, and capabilities, that are in line with their actual design. Explanations and transparent behaviour of the system should inspire trust and, ultimately, lead to more persuasive recommendations. Here, explanations convey reasons why a recommendation is given or how the system forms its recommendations. This paper focuses on the question how such claims about effectiveness of explanations can be evaluated. Accordingly, we investigate various models that are used to assess the effects of explanations and recommendations. We discuss objective and subjective measurement and argue that both are needed. We define a set of metrics for measuring the effectiveness of explanations and recommendations. The feasibility of using these metrics is discussed in the context of a specific explainable recommender system in the food and health domain.

Cite As: Joris Hulstin, Igor Tchappi, Amro Najjar, and ReyhanAydoğan, “Metrics for Evaluating Explainable Recommender Systems”, Explainable and Transparent AI and Multi-Agent Systems EXTRAAMAS 2023, Lecture Notes in Computer Science, pp. 212-230, 2023.

Topic: Evaluation, Metrics, Explainable AI, Recommender systems

Type: Workshop

Links: https://link.springer.com/chapter/10.1007/978-3-031-40878-6_3

Bibtex: @InProceedings{Ciatto_ETAMAS_2023, author={Ciatto, Giovanni and Magnini, Matteo and Buzcu, Berk and Aydo{u{g}}an, Reyhan and Omicini, Andrea}, editor={Calvaresi, Davide and Najjar, Amro and Omicini, Andrea and Aydogan, Reyhan and Carli, Rachele and Ciatto, Giovanni and Mualla, Yazan and Fr{"a}mling, Kary}, title={A General-Purpose Protocol for Multi-agent Based Explanations}, booktitle={Explainable and Transparent AI and Multi-Agent Systems}, year={2023}, publisher={Springer Nature Switzerland}, address={Cham}, pages={38--58}, abstract={Building on prior works on explanation negotiation protocols, this paper proposes a general-purpose protocol for multi-agent systems where recommender agents may need to provide explanations for their recommendations. The protocol specifies the roles and responsibilities of the explainee and the explainer agent and the types of information that should be exchanged between them to ensure a clear and effective explanation. However, it does not prescribe any particular sort of recommendation or explanation, hence remaining agnostic w.r.t. such notions. Novelty lays in the extended support for both ordinary and contrastive explanations, as well as for the situation where no explanation is needed as none is requested by the explainee.}, isbn={978-3-031-40878-6} }

Abstract: Building on prior works on explanation negotiation protocols, this paper proposes a general-purpose protocol for multi-agent systems where recommender agents may need to provide explanations for their recommendations. The protocol specifies the roles and responsibilities of the explainee and the explainer agent and the types of information that should be exchanged between them to ensure a clear and effective explanation. However, it does not prescribe any particular sort of recommendation or explanation, hence remaining agnostic w.r.t. such notions. Novelty lays in the extended support for both ordinary and contrastive explanations, as well as for the situation where no explanation is needed as none is requested by the explainee. Accordingly, we formally present and analyse the protocol, motivating its design and discussing its generality. We also discuss the reification of the protocol into a re-usable software library, namely PYXMAS, which is meant to support developers willing to build explainable MAS leveraging our protocol. Finally, we discuss how custom notions of recommendation and explanation can be easily plugged into PYXMAS.

Cite As: Giovanni Ciatto, Matteo Magnini, Berk Buzcu, Reyhan Aydoğan, and Andrea Omicini, “Modular Framework for Explainable Recommenders”, Explainable and Transparent AI and Multi-Agent Systems EXTRAAMAS 2023, Lecture Notes in Computer Science, pp. 38-58, 2023.

Topic: XAI, recommender systems, multi-agent systems, explanation protocols, SPADE, PYXMAS

Type: Workshop

Links: https://link.springer.com/chapter/10.1007/978-981-99-0561-4_3

Bibtex: @InProceedings{Aydogan_RAAN_2023_DecisionSupport, author={Aydo{u{g}}an, Reyhan and Jonker, Catholijn M.}, editor={Hadfi, Rafik and Aydo{u{g}}an, Reyhan and Ito, Takayuki and Arisaka, Ryuta}, title={A Survey of Decision Support Mechanisms for Negotiation}, booktitle={Recent Advances in Agent-Based Negotiation: Applications and Competition Challenges}, year={2023}, publisher={Springer Nature Singapore}, address={Singapore}, pages={30--51}, abstract={This paper introduces a dependency analysis and a categorization of conceptualized and existing economic decision support mechanisms for negotiation. The focus of our survey is on economic decision support mechanisms, although some behavioural support mechanisms were included, to recognize the important work in that area. We categorize support mechanisms from four different aspects: (i) economic versus behavioral decision support, (ii) analytical versus strategical support, (iii) active versus passive support and (iv) implicit versus explicit support. Our survey suggests that active mechanisms would be more effective than passive ones, and that implicit mechanisms can shield the user from mathematical complexities. Furthermore, we provide a list of existing economic support mechanisms.}, isbn={978-981-99-0561-4} }

Abstract: This paper introduces a dependency analysis and a categorization of conceptualized and existing economic decision support mechanisms for negotiation. The focus of our survey is on economic decision support mechanisms, although some behavioural support mechanisms were included, to recognize the important work in that area. We categorize support mechanisms from four different aspects: (i) economic versus behavioral decision support, (ii) analytical versus strategical support, (iii) active versus passive support and (iv) implicit versus explicit support. Our survey suggests that active mechanisms would be more effective than passive ones, and that implicit mechanisms can shield the user from mathematical complexities. Furthermore, we provide a list of existing economic support mechanisms.

Cite As: Reyhan Aydoğan and Catholijn M. Jonker, “A Survey of Decision Support Mechanisms for Negotiation”, In Hadfi, Aydoğan, Ito, and Arisaka (eds), Recent Advances in Agent-based Negotiation: Applications and Competition Challenges, Springer, Available in 2023.

Topic: Negotiation support, Economic decision support, Survey

Type: Workshop

Links: https://link.springer.com/chapter/10.1007/978-981-99-0561-4_4

Bibtex: @InProceedings{Aydogan_RAAN_2023_BiddingSupport, author={Aydo{u{g}}an, Reyhan and Jonker, Catholijn M.}, editor={Hadfi, Rafik and Aydo{u{g}}an, Reyhan and Ito, Takayuki and Arisaka, Ryuta}, title={Bidding Support by the Pocket Negotiator Improves Negotiation Outcomes}, booktitle={Recent Advances in Agent-Based Negotiation: Applications and Competition Challenges}, year={2023}, publisher={Springer Nature Singapore}, address={Singapore}, pages={52--83}, abstract={This paper presents the negotiation support mechanisms provided by the Pocket Negotiator (PN) and an elaborate empirical evaluation of the economic decision support (EDS) mechanisms during the bidding phase of negotiations as provided by the PN. Some of these support mechanisms are offered actively, some passively. With passive support we mean that the user only gets that support by clicking a button, whereas active support is provided without prompting. Our results show, that PN improves negotiation outcomes, counters cognitive depletion, and encourages exploration of potential outcomes. We found that the active mechanisms were used more effectively than the passive ones and, overall, the various mechanisms were not used optimally, which opens up new avenues for research. As expected, the participants with higher negotiation skills outperformed the other groups, but still they benefited from PN support. Our experimental results show that people with enough technical skills and with some basic negotiation knowledge will benefit most from PN support. Our results also show that the cognitive depletion effect is reduced by Pocket Negotiator support. The questionnaire taken after the experiment shows that overall the participants found Pocket Negotiator easy to interact with, that it made them negotiate more quickly and that it improves their outcome. Based on our findings, we recommend to 1) provide active support mechanisms (push) to nudge users to be more effective, and 2) provide support mechanisms that shield the user from mathematical complexities.}, isbn={978-981-99-0561-4} }

Abstract: This paper presents the negotiation support mechanisms provided by the Pocket Negotiator (PN) and an elaborate empirical evaluation of the economic decision support (EDS) mechanisms during the bidding phase of negotiations as provided by the PN. Some of these support mechanisms are offered actively, some passively. With passive support we mean that the user only gets that support by clicking a button, whereas active support is provided without prompting. Our results show, that PN improves negotiation outcomes, counters cognitive depletion, and encourages exploration of potential outcomes. We found that the active mechanisms were used more effectively than the passive ones and, overall, the various mechanisms were not used optimally, which opens up new avenues for research. As expected, the participants with higher negotiation skills outperformed the other groups, but still they benefited from PN support. Our experimental results show that people with enough technical skills and with some basic negotiation knowledge will benefit most from PN support. Our results also show that the cognitive depletion effect is reduced by Pocket Negotiator support. The questionnaire taken after the experiment shows that overall the participants found Pocket Negotiator easy to interact with, that it made them negotiate more quickly and that it improves their outcome. Based on our findings, we recommend to 1) provide active support mechanisms (push) to nudge users to be more effective, and 2) provide support mechanisms that shield the user from mathematical complexities.

Cite As: Reyhan Aydoğan and Catholijn M. Jonker, “Bidding Support by the Pocket Negotiator Improves Negotiation Outcomes”, In Hadfi, Aydoğan, Ito, and Arisaka (eds), Recent Advances in Agent-based Negotiation: Applications and Competition Challenges, Springer, Available 2023.

Topic: Negotiation support, Bidding support, Experimental performance evaluation, User experience analysis

Type: Workshop

Links: https://link.springer.com/chapter/10.1007/978-981-99-0561-4_5

Bibtex: @InProceedings{Aydogan_RAAN_2023_ANACResults, author={Aydo{u{g}}an, Reyhan and Baarslag, Tim and Fujita, Katsuhide and Hoos, Holger H. and Jonker, Catholijn M. and Mohammad, Yasser and Renting, Bram M.}, editor={Hadfi, Rafik and Aydo{u{g}}an, Reyhan and Ito, Takayuki and Arisaka, Ryuta}, title={The 13th International Automated Negotiating Agent Competition Challenges and Results}, booktitle={Recent Advances in Agent-Based Negotiation: Applications and Competition Challenges}, year={2023}, publisher={Springer Nature Singapore}, address={Singapore}, pages={87--101}, abstract={An international competition for negotiating agents has been organized for years to facilitate research in agent-based negotiation and to encourage the design of negotiating agents that can operate in various scenarios. The 13th International Automated Negotiating Agents Competition (ANAC 2022) was held in conjunction with IJCAI2022. In ANAC2022, we had two leagues: Automated Negotiation League (ANL) and Supply Chain Management League (SCML). For the ANL, the participants designed a negotiation agent that can learn from the previous bilateral negotiation sessions it was involved in. In contrast, the research challenge was to make the right decisions to maximize the overall profit in a supply chain environment, such as determining with whom and when to negotiate. This chapter describes the overview of ANL and SCML in ANAC2022, and reports the results of each league, respectively.}, isbn={978-981-99-0561-4} }

Abstract: An international competition for negotiating agents has been organized for years to facilitate research in agent-based negotiation and to encourage the design of negotiating agents that can operate in various scenarios. The 13th International Automated Negotiating Agents Competition (ANAC 2022) was held in conjunction with IJCAI2022. In ANAC2022, we had two leagues: Automated Negotiation League (ANL) and Supply Chain Management League (SCML). For the ANL, the participants designed a negotiation agent that can learn from the previous bilateral negotiation sessions it was involved in. In contrast, the research challenge was to make the right decisions to maximize the overall profit in a supply chain environment, such as determining with whom and when to negotiate. This chapter describes the overview of ANL and SCML in ANAC2022, and reports the results of each league, respectively.

Cite As: Reyhan Aydoğan, Tim Baarslag, Katsuhide Fujita, Holger H. Hoos, Catholijn M. Jonker, Yasser Mohammad, and Bram M., “The 13th International Automated Negotiating Agent Competition Challenges and Results”, In Hadfi, Aydoğan, Ito, and Arisaka (eds), Recent Advances in Agent-based Negotiation: Applications and Competition Challenges, Springer, Available in 2023.

Topic: Agent-Based Negotiation, Automated Negotiating Agent Competition (ANAC), Supply Chain Management League (SCML), Profit Maximization

Type: Workshop

Links: https://link.springer.com/chapter/10.1007/978-981-99-0561-4_6

Bibtex: @InProceedings{Aydogan_RAAN_2023_AhBuNeAgent, author={Y{i}ld{i}r{i}m, Ahmet Burak and Sunman, Nezih and Aydo{u{g}}an, Reyhan}, editor={Hadfi, Rafik and Aydo{u{g}}an, Reyhan and Ito, Takayuki and Arisaka, Ryuta}, title={AhBuNe Agent: Winner of the Eleventh International Automated Negotiating Agent Competition (ANAC 2020)}, booktitle={Recent Advances in Agent-Based Negotiation: Applications and Competition Challenges}, year={2023}, publisher={Springer Nature Singapore}, address={Singapore}, pages={102--118}, abstract={The International Automated Negotiating Agent Competition introduces a new challenge each year to facilitate the research on agent-based negotiation and provide a test benchmark. ANAC 2020 addressed the problem of designing effective agents that do not know their users' complete preferences in addition to their opponent's negotiation strategy. Accordingly, this paper presents the negotiation strategy of the winner agent called ``AhBuNe Agent''. The proposed heuristic-based bidding strategy checks whether it has sufficient orderings to reason about its complete preferences and accordingly decides whether to sacrifice some utility in return for preference elicitation. While making an offer, it uses the most-desired known outcome as a reference and modifies the content of the bid by adopting a concession-based strategy. By analyzing the content of the given ordered bids, the importance ranking of the issues is estimated. As our agent adopts a fixed time-based concession strategy and takes the estimated issue importance ranks into account, it determines to what extent the issues are to be modified. The evaluation results of the ANAC 2020 show that our agent beats the other participating agents in terms of the received individual score.}, isbn={978-981-99-0561-4} }

Abstract: The International Automated Negotiating Agent Competition introduces a new challenge each year to facilitate the research on agent-based negotiation and provide a test benchmark. ANAC 2020 addressed the problem of designing effective agents that do not know their users’ complete preferences in addition to their opponent’s negotiation strategy. Accordingly, this paper presents the negotiation strategy of the winner agent called “AhBuNe Agent”. The proposed heuristic-based bidding strategy checks whether it has sufficient orderings to reason about its complete preferences and accordingly decides whether to sacrifice some utility in return for preference elicitation. While making an offer, it uses the most-desired known outcome as a reference and modifies the content of the bid by adopting a concession-based strategy. By analyzing the content of the given ordered bids, the importance ranking of the issues is estimated. As our agent adopts a fixed time-based concession strategy and takes the estimated issue importance ranks into account, it determines to what extent the issues are to be modified. The evaluation results of the ANAC 2020 show that our agent beats the other participating agents in terms of the received individual score.

Cite As: Ahmet Burak Yildirim, Nezih Sunman, Reyhan Aydoğan, “AhBuNe Agent: Winner of the Eleventh International Automated Negotiating Agent Competition (ANAC 2020)”, In Hadfi, Aydoğan, Ito, and Arisaka (eds), Recent Advances in Agent-based Negotiation: Applications and Competition Challenges, Springer, Available in 2023.

Topic: Automated negotiation, Agent competition, Partial preference ordering, Negotiation strategy

Type: Workshop

Links: https://link.springer.com/chapter/10.1007/978-3-030-82017-6_20#citeas

Bibtex: @InProceedings{Calvaresi_ETAMAS_2021, author={Calvaresi, Davide and Ciatto, Giovanni and Najjar, Amro and Aydo{u{g}}an, Reyhan and Van der Torre, Leon and Omicini, Andrea and Schumacher, Michael}, editor={Calvaresi, Davide and Najjar, Amro and Winikoff, Michael and Fr{"a}mling, Kary}, title={Expectation: Personalized Explainable Artificial Intelligence for Decentralized Agents with Heterogeneous Knowledge}, booktitle={Explainable and Transparent AI and Multi-Agent Systems}, year={2021}, publisher={Springer International Publishing}, address={Cham}, pages={331--343}, abstract={Explainable AI (XAI) has emerged in recent years as a set of techniques and methodologies to interpret and explain machine learning (ML) predictors. To date, many initiatives have been proposed. Nevertheless, current research efforts mainly focus on methods tailored to specific ML tasks and algorithms, such as image classification and sentiment analysis. However, explanation techniques are still embryotic, and they mainly target ML experts rather than heterogeneous end-users. Furthermore, existing solutions assume data to be centralised, homogeneous, and fully/continuously accessible---circumstances seldom found altogether in practice. Arguably, a system-wide perspective is currently missing.}, isbn={978-3-030-82017-6} }

Abstract: Explainable AI (XAI) has emerged in recent years as a set of techniques and methodologies to interpret and explain machine learning (ML) predictors. To date, many initiatives have been proposed. Nevertheless, current research efforts mainly focus on methods tailored to specific ML tasks and algorithms, such as image classification and sentiment analysis. However, explanation techniques are still embryotic, and they mainly target ML experts rather than heterogeneous end-users. Furthermore, existing solutions assume data to be centralised, homogeneous, and fully/continuously accessible—circumstances seldom found altogether in practice. Arguably, a system-wide perspective is currently missing. The project named “Personalized Explainable Artificial Intelligence for Decentralized Agents with Heterogeneous Knowledge ” (EXPECTATION) aims at overcoming such limitations. This manuscript presents the overall objectives and approach of the EXPECTATION project, focusing on the theoretical and practical advance of the state of the art of XAI towards the construction of personalised explanations in spite of decentralisation and heterogeneity of knowledge, agents, and explainees (both humans or virtual). To tackle the challenges posed by personalisation, decentralisation, and heterogeneity, the project fruitfully combines abstractions, methods, and approaches from the multi-agent systems, knowledge extraction/injection, negotiation, argumentation, and symbolic reasoning communities.

Cite As: Davide Calvaresi, Giovanni Ciatt, Amro Najjar, Reyhan Aydoğan, Leon Van der Torre, Andrea Omicini, and Michael Schumache, “Expectation: Personalized Explainable Artificial Intelligence for Decentralized Agents with Heterogeneous Knowledge”, In: Calvaresi D., Najjar A., Winikoff M., Främling K. (eds) Explainable and Transparent AI and Multi-Agent Systems. EXTRAAMAS 2021. Lecture Notes in Computer Science, vol 12688. Springer, Cham., pp. pp 331-343, 2021.

Topic: Multi-agent systems, eXplanable AI, CHIST-ERA IV, Personalisation, Decentralisation, EXPECTATION

Type: Workshop

Links: https://link.springer.com/chapter/10.1007/978-981-16-0471-3_1

Bibtex: @InProceedings{Gungor_RAAN_2021, author={G{"u}ng{"o}r, Onat and {c{C}}akan, Umut and Aydo{u{g}}an, Reyhan and {""O}zturk, Pinar}, editor={Aydo{u{g}}an, Reyhan and Ito, Takayuki and Moustafa, Ahmed and Otsuka, Takanobu and Zhang, Minjie}, title={Effect of Awareness of Other Side's Gain on Negotiation Outcome, Emotion, Argument, and Bidding Behavior}, booktitle={Recent Advances in Agent-based Negotiation}, year={2021}, publisher={Springer Singapore}, address={Singapore}, pages={3--20}, abstract={Designing agents aiming to negotiate with human counterparts requires additional factors. In this work, we analyze the main elements of human negotiations in a structured human experiment. Particularly, we focus on studying the effect of negotiators being aware of the other side's gain on the bidding behavior and the negotiation outcome. We compare the negotiations in two settings where one allows human negotiators to see their opponent's utility and the other does not. Furthermore, we study what kind of emotional state expressed and arguments sent in those setups. We rigorously discuss the findings from our experiments.}, isbn={978-981-16-0471-3} }

Abstract: Designing agents aiming to negotiate with human counterparts requires additional factors. In this work, we analyze the main elements of human negotiations in a structured human experiment. Particularly, we focus on studying the effect of negotiators being aware of the other side’s gain on the bidding behavior and the negotiation outcome. We compare the negotiations in two settings where one allows human negotiators to see their opponent’s utility and the other does not. Furthermore, we study what kind of emotional state expressed and arguments sent in those setups. We rigorously discuss the findings from our experiments.

Cite As: Onat Güngör, Umut Çakan, Reyhan Aydoğan and Pınar Öztürk, “Effect of Awareness of Other Side’s Gain on Negotiation Outcome, Emotion, Argument and Bidding Behavior”, In Aydoğan et al. (eds), Recent Advances in Agent-based Negotiation, pp. 3-20, 2021.

Type: Workshop

Links: https://link.springer.com/chapter/10.1007/978-981-15-5869-6_1

Bibtex: @InProceedings{Aydogan_AAN_2021, author={Aydo{u{g}}an, Reyhan and Keskin, Onur and {c{C}}akan, Umut}, editor={Ito, Takayuki and Zhang, Minjie and Aydo{u{g}}an, Reyhan}, title={Let's Negotiate with Jennifer! Towards a Speech-Based Human-Robot Negotiation}, booktitle={Advances in Automated Negotiations}, year={2021}, publisher={Springer Singapore}, address={Singapore}, pages={3--16}, abstract={Social robots are becoming prevalent in our society, and most of the time, they need to interact with humans in order to accomplish their tasks. Negotiation is one of the inevitable processes they need to be involved to make joint decisions with their human counterparts when there is a conflict of interest between them. This paper pursues a novel approach for a humanoid robot to negotiate with humans efficiently via speech. In this work, we propose a speech-based negotiation protocol in which agents make their offers in a turn-taking fashion via speech. We present a variant of time-based concession bidding strategy for the humanoid robot and evaluated the performance of the robot against human counterpart in human-robot negotiation experiments.}, isbn={978-981-15-5869-6} }

Abstract: Social robots are becoming prevalent in our society, and most of the time, they need to interact with humans in order to accomplish their tasks. Negotiation is one of the inevitable processes they need to be involved to make joint decisions with their human counterparts when there is a conflict of interest between them. This paper pursues a novel approach for a humanoid robot to negotiate with humans efficiently via speech. In this work, we propose a speech-based negotiation protocol in which agents make their offers in a turn-taking fashion via speech. We present a variant of time-based concession bidding strategy for the humanoid robot and evaluated the performance of the robot against human counterpart in human-robot negotiation experiments.

Cite As: Reyhan Aydoğan, Onur Keskin and Umut Çakan, “Let’s negotiate with Jennifer! Towards a Speech-based Human-Robot Negotiation”, In Ito, T., Zhang, M. and Aydoğan (eds), Advances in Automated Negotiations, pp. 3-16, 2020.

Type: Workshop

Links: https://link.springer.com/chapter/10.1007/978-981-15-5869-6_7

Bibtex: @InProceedings{Aydogan_AAN_2021_ANAC2017, author={Aydo{u{g}}an, Reyhan and Fujita, Katsuhide and Baarslag, Tim and Jonker, Catholijn M. and Ito, Takayuki}, editor={Ito, Takayuki and Zhang, Minjie and Aydo{u{g}}an, Reyhan}, title={ANAC 2017: Repeated Multilateral Negotiation League}, booktitle={Advances in Automated Negotiations}, year={2021}, publisher={Springer Singapore}, address={Singapore}, pages={101--115}, abstract={The Automated Negotiating Agents Competition (ANAC) is annually organized competition to facilitate the research on automated negotiation. This paper presents the ANAC 2017 Repeated Multilateral Negotiation League. As human negotiators do, agents are supposed to learn from their previous negotiations and improve their negotiation skills over time. Especially, when they negotiate with the same opponent on the same domain, they can adopt their negotiation strategy according to their past experiences. They can adjust their acceptance threshold or bidding strategy accordingly. In ANAC 2017, participants aimed to develop such a negotiating agent. Accordingly, this paper describes the competition settings and results with a brief description of the winner negotiation strategies.}, isbn={978-981-15-5869-6} }

Abstract: The Automated Negotiating Agents Competition (ANAC) is annually organized competition to facilitate the research on automated negotiation. This paper presents the ANAC 2017 Repeated Multilateral Negotiation League. As human negotiators do, agents are supposed to learn from their previous negotiations and improve their negotiation skills over time. Especially, when they negotiate with the same opponent on the same domain, they can adopt their negotiation strategy according to their past experiences. They can adjust their acceptance threshold or bidding strategy accordingly. In ANAC 2017, participants aimed to develop such a negotiating agent. Accordingly, this paper describes the competition settings and results with a brief description of the winner negotiation strategies.

Cite As: Reyhan Aydoğan, Katsuhide Fujita, Tim Baarslag, Catholijn M. Jonker and Takayuki Ito, “ANAC 2017: Repeated Multilateral Negotiation League”, In Ito, T., Zhang, M. and Aydoğan (eds), Advances in Automated Negotiations, pp. 101-115, 2020.

Type: Workshop

Links: https://link.springer.com/chapter/10.1007/978-981-15-5869-6_3

Bibtex: @Inbook{Fujita_RAACAN_2017, author={Fujita, Katsuhide and Aydo{u{g}}an, Reyhan and Baarslag, Tim and Hindriks, Koen and Ito, Takayuki and Jonker, Catholijn}, editor={Fujita, Katsuhide and Bai, Quan and Ito, Takayuki and Zhang, Minjie and Ren, Fenghui and Aydo{u{g}}an, Reyhan and Hadfi, Rafik}, title={The Sixth Automated Negotiating Agents Competition (ANAC 2015)}, bookTitle={Modern Approaches to Agent-based Complex Automated Negotiation}, year={2017}, publisher={Springer International Publishing}, address={Cham}, pages={139--151}, abstract={In May 2015, we organized the Sixth International Automated Negotiating Agents Competition (ANAC 2015) in conjunction with AAMAS 2015. ANAC is an international competition that challenges researchers to develop a successful automated negotiator for scenarios where there is incomplete information about the opponent. One of the goals of this competition is to help steer the research in the area of multi-issue negotiations, and to encourage the design of generic negotiating agents that are able to operate in a variety of scenarios. 24 teams from 9 different institutes competed in ANAC 2015. This chapter describes the participating agents and the setup of the tournament, including the different negotiation scenarios that were used in the competition. We report on the results of the qualifying and final round of the tournament.}, isbn={978-3-319-51563-2}, doi={10.1007/978-3-319-51563-2_9}, url={https://doi.org/10.1007/978-3-319-51563-2_9} }

Abstract: This paper presents the Deniz agent that has been specifically designed to support human negotiators in their bidding. The design of Deniz is done with the criteria of robustness and the availability of small data, due to a small number of negotiation rounds in mind. Deniz’s bidding strategy is based on an existing optimal concession strategy that concedes in relation to the expected duration of the negotiation. This accounts for the small data and small number of rounds. Deniz deploys an adaptive behavior-based mechanism to make it robust against exploitation. We tested Deniz against typical bidding strategies and against human negotiators. Our evaluation shows that Deniz is robust against exploitation and gains statistically significant higher utilities than human test subjects, even though it is not designed specifically to get the highest utility against humans.

Cite As: Catholijn M. Jonker and Reyhan Aydoğan, “Deniz: A Robust Bidding Strategy for Negotiation Support Systems”, In Ito, T., Zhang, M. and Aydoğan (eds), Advances in Automated Negotiations, pp 29-44, 2020.

Type: Workshop

Links: https://link.springer.com/chapter/10.1007/978-3-319-57285-7_2

Bibtex: @Inbook{Aydogan_MAACAN_2017, author={Aydo{u{g}}an, Reyhan and Festen, David and Hindriks, Koen V. and Jonker, Catholijn M.}, editor={Fujita, Katsuhide and Bai, Quan and Ito, Takayuki and Zhang, Minjie and Ren, Fenghui and Aydo{u{g}}an, Reyhan and Hadfi, Rafik}, title={Alternating Offers Protocols for Multilateral Negotiation}, bookTitle={Modern Approaches to Agent-based Complex Automated Negotiation}, year={2017}, publisher={Springer International Publishing}, address={Cham}, pages={153--167}, abstract={This paper presents a general framework for multilateral turn-taking protocols and two fully specified protocols namely Stacked Alternating Offers Protocol (SAOP) and Alternating Multiple Offers Protocol (AMOP). In SAOP, agents can make a bid, accept the most recent bid or walk way (i.e., end the negotiation without an agreement) when it is their turn. AMOP has two different phases: bidding and voting. The agents make their bid in the bidding phase and vote the underlying bids in the voting phase. Unlike SAOP, AMOP does not support walking away option. In both protocols, negotiation ends when the negotiating agents reach a joint agreement or some deadline criterion applies. The protocols have been evaluated empirically, showing that SAOP outperforms AMOP with the same type of conceder agents in a time-based deadline setting. SAOP was used in the ANAC 2015 competition for automated negotiating agents.}, isbn={978-3-319-51563-2}, doi={10.1007/978-3-319-51563-2_10}, url={https://doi.org/10.1007/978-3-319-51563-2_10} }

Abstract: Classically, disciplines like negotiation and decision making have focused on reaching Pareto optimal solutions due to its stability and efficiency properties. Despite the fact that many practical and theoretical algorithms have successfully attempted to provide Pareto optimal solutions, they have focused on attempting to reach Pareto Optimality using horizontal approaches, where optimality is calculated taking into account every participant at the same time. Sometimes, this may prove to be a difficult task (e.g., conflict, mistrust, no information sharing, etc.). In this paper, we explore the possibility of achieving Pareto Optimal outcomes in a group by using a bottom-up approach: discovering Pareto optimal outcomes by interacting in subgroups. We analytically show that the set of Pareto optimal outcomes in a group covers the Pareto optimal outcomes within its subgroups. This theoretical finding can be applied in a variety of scenarios such as negotiation teams, multi-party negotiation, and team formation to social recommendation. Additionally, we empirically test the validity and practicality of this proof in a variety of decision making domains and analyze the usability of this proof in practical situations.

Cite As: Sanchez-Anguix V., Aydoğan R., Baarslag T., Jonker C.M. Can We Reach Pareto Optimal Outcomes Using Bottom-Up Approaches?. In: Aydoğan R., Baarslag T., Gerding E., Jonker C., Julian V., Sanchez-Anguix V. (eds) Conflict Resolution in Decision Making. COREDEMA 2016. Lecture Notes in Computer Science, vol 10238. Springer, pp. 19-35., 2017.

Type: Workshop

Links: https://link.springer.com/chapter/10.1007/978-3-319-51563-2_9

Bibtex: @inbook{Aydogan_ANAC_2016, author = {Aydo{u{g}}an, Reyhan and Baarslag, Tim and Jonker, Catholijn and Fujita, Katsuhide and Ito, Takayuki and Hadfi, Rafik and Hayakawa, Kohei}, year = {2016}, month = {07}, publisher = {Springer}, title = {A Baseline for Nonlinear Bilateral Negotiations: The full results of the agents competing in ANAC 2014}, isbn = {9781681085029}, doi = {10.2174/9781681085029117010007} }

Abstract: In May 2015, we organized the Sixth International Automated Negotiating Agents Competition (ANAC 2015) in conjunction with AAMAS 2015. ANAC is an international competition that challenges researchers to develop a successful automated negotiator for scenarios where there is incomplete information about the opponent. One of the goals of this competition is to help steer the research in the area of multi-issue negotiations, and to encourage the design of generic negotiating agents that are able to operate in a variety of scenarios. 24 teams from 9 different institutes competed in ANAC 2015. This chapter describes the participating agents and the setup of the tournament, including the different negotiation scenarios that were used in the competition. We report on the results of the qualifying and final round of the tournament.

Cite As: Fujita, K., Aydoğan, R., Baarslag, T., Hindriks, K., Ito, T., and Jonker, C. M., The Sixth Automated Negotiating Agents Competition (ANAC 2015). In Fujita, K, Bai, Q, Ito, T, Zhang, M, Ren, F, Aydoğan, R, & Hadfi, R(Eds.), Modern Approaches to Agent-based Complex Automated Negotiation, 2017.

Type: Workshop

Links: https://link.springer.com/chapter/10.1007/978-3-319-51563-2_10

Bibtex: @Inbook{Fujita_RAACAN_2016, author={Fujita, Katsuhide and Aydo{u{g}}an, Reyhan and Baarslag, Tim and Ito, Takayuki and Jonker, Catholijn}, editor={Fukuta, Naoki and Ito, Takayuki and Zhang, Minjie and Fujita, Katsuhide and Robu, Valentin}, title={The Fifth Automated Negotiating Agents Competition (ANAC 2014)}, bookTitle={Recent Advances in Agent-based Complex Automated Negotiation}, year={2016}, publisher={Springer International Publishing}, address={Cham}, pages={211-224}, abstract={In May 2014, we organized the Fifth International Automated Negotiating Agents Competition (ANAC 2014) in conjunction with AAMAS 2014. ANAC is an international competition that challenges researchers to develop a successful automated negotiator for scenarios where there is incomplete information about the opponent. One of the goals of this competition is to help steer the research in the area of bilateral multi-issue negotiations, and to encourage the design of generic negotiating agents that are able to operate in a variety of scenarios. 21 teams from 13 different institutes competed in ANAC 2014. This chapter describes the participating agents and the setup of the tournament, including the different negotiation scenarios that were used in the competition. We report on the results of the qualifying and final round of the tournament.}, isbn={978-3-319-30307-9}, doi={10.1007/978-3-319-30307-9_13}, url={https://doi.org/10.1007/978-3-319-30307-9_13} }

Abstract: This paper presents a general framework for multilateral turn-taking protocols and two fully specified protocols namely Stacked Alternating Offers Protocol (SAOP) and Alternating Multiple Offers Protocol (AMOP). In SAOP, agents can make a bid, accept the most recent bid or walk way (i.e., end the negotiation without an agreement) when it is their turn. AMOP has two different phases: bidding and voting. The agents make their bid in the bidding phase and vote the underlying bids in the voting phase. Unlike SAOP, AMOP does not support walking away option. In both protocols, negotiation ends when the negotiating agents reach a joint agreement or some deadline criterion applies. The protocols have been evaluated empirically, showing that SAOP outperforms AMOP with the same type of conceder agents in a time-based deadline setting. SAOP was used in the ANAC 2015 competition for automated negotiating agents.

Cite As: Reyhan Aydoğan, David Festen,Koen Hindriks, and C. M. Jonker, “Alternating Offers Protocol for Multilateral Negotiation”, In K. Fujita, Q. Bai, T. Ito, M. Zhang, F. Ren, R. Aydoğan & R. Hadfi (Editors). Modern Approaches to Agent-based Complex Automated Negotiation, Springer Japan, pp. 153-167, 2017.

Type: Workshop

Links: https://www.eurekaselect.com/154719/chapter/a-baseline-for-nonlinear-bilateral-negotiations%3A-the-full-results-of-the-agents-competing-in-anac-201

Bibtex: @Inbook{Aydogan_NIACAN_2014, author={Aydo{u{g}}an, Reyhan and Hindriks, Koen V. and Jonker, Catholijn M.}, editor={Marsa-Maestre, Ivan and Lopez-Carmona, Miguel A. and Ito, Takayuki and Zhang, Minjie and Bai, Quan and Fujita, Katsuhide}, title={Multilateral Mediated Negotiation Protocols with Feedback}, bookTitle={Novel Insights in Agent-based Complex Automated Negotiation}, year={2014}, publisher={Springer Japan}, address={Tokyo}, pages={43--59}, abstract={When more than two participants have a conflict of interest, finding a mutual agreement may entail a time consuming process especially when the number of participants is high. Automated negotiation tools can play a key role in providing effective solutions. This paper presents two variants of feedback based multilateral negotiation protocol in which a mediator agent generates bids and negotiating agents give their feedback about those bids. We investigate different types of feedback given to the mediator. The mediator uses agents' feedback to models each agent's preferences and accordingly generates well-targeted bids over time rather than arbitrary bids. Furthermore, the paper investigates the performance of the protocols in an experimental setting. Experimental results show that the proposed protocols result in a reasonably good outcome for all agents in a relatively short time.}, isbn={978-4-431-54758-7}, doi={10.1007/978-4-431-54758-7_3}, url={https://doi.org/10.1007/978-4-431-54758-7_3} }

Abstract: In the past few years, there is a growing interest in automated negotiationin which software agents facilitate negotiation on behalf of their users and try to reach joint agreements. The potential value of developing such mechanisms becomes enormous when negotiation domain is too complex for humans to find agreements (e.g. e-commerce) and when software components need to reach agreements to work together (e.g. web-service composition). Here, one of the major challenges is to design agents that are able to deal with incomplete information about their opponents in negotiation as well as to effectively negotiate on their users’ behalves. To facilitate the research in this field, an automated negotiating agent competition has been organized yearly. This paper introduces the research challenges in Automated Negotiating Agent Competition (ANAC) 2014 and explains the competition set up and results. Furthermore, a detailed analysis of the best performing five agent has been examined.

Cite As: Reyhan Aydoğan, Catholijn M. Jonker, Katsuhide Fujita, Tim Baarslag, Takayuki Ito, Rafik Hadfi, and Kohei Hayakawa. A Baseline for Nonlinear Bilateral Negotiations: The full results of the agents competing in ANAC 2014. In Faria N. Mofakham (editor). Frontiers in Artificial Intelligence: Intelligent Computational Systems, Bentham Science Publishers, pp. 96-122, 2017.

Type: Workshop

Links: http://link.springer.com/chapter/10.1007%2F978-3-319-30307-9_13

Bibtex: @Inbook{Sanchez-Anguix_CRDM_2017, author={Sanchez-Anguix, Victor and Aydo{u{g}}an, Reyhan and Julian, Vicente and Jonker, Catholijn M.}, editor={Marsa-Maestre, Ivan and Lopez-Carmona, Miguel A. and Ito, Takayuki and Zhang, Minjie and Bai, Quan and Fujita, Katsuhide}, title={Intra-Team Strategies for Teams Negotiating Against Competitor, Matchers, and Conceders}, bookTitle={Novel Insights in Agent-based Complex Automated Negotiation}, year={2014}, publisher={Springer Japan}, address={Tokyo}, pages={3--22}, abstract={Under some circumstances, a group of individuals may need to negotiate together as a negotiation team against another party. Unlike bilateral negotiation between two individuals, this type of negotiations entails to adopt an intra-team strategy for negotiation teams in order to make team decisions and accordingly negotiate with the opponent. It is crucial to be able to negotiate successfully with heterogeneous opponents since opponents' negotiation strategy and behavior may vary in an open environment. While one opponent might collaborate and concede over time, another may not be inclined to concede. This paper analyzes the performance of recently proposed intra-team strategies for negotiation teams against different categories of opponents: competitors, matchers, and conceders. Furthermore, it provides an extension of the negotiation tool Genius for negotiation teams in bilateral settings. Consequently, this work facilitates research in negotiation teams.}, isbn={978-4-431-54758-7}, doi={10.1007/978-4-431-54758-7_1}, url={https://doi.org/10.1007/978-4-431-54758-7_1} }

Abstract: In May 2014, we organized the Fifth International Automated Negotiating Agents Competition (ANAC 2014) in conjunction with AAMAS 2014. ANAC is an international competition that challenges researchers to develop a successful automated negotiator for scenarios where there is incomplete information about the opponent. One of the goals of this competition is to help steer the research in the area of bilateral multi-issue negotiations, and to encourage the design of generic negotiating agents that are able to operate in a variety of scenarios. 21 teams from 13 different institutes competed in ANAC 2014. This chapter describes the participating agents and the setup of the tournament, including the different negotiation scenarios that were used in the competition. We report on the results of the qualifying and final round of the tournament.

Cite As: Katsuhide Fujita, Reyhan Aydoğan, Tim Baarslag, Takayuki Ito, and Catholijn M. Jonker, “The Fifth Automated Negotiating Agents Competition (ANAC 2014)”, In Fukuta, N, Ito, T, Zhang, M, Fujita, K, & Robu, V (Editors.), Recent Advances in Agent-based Complex Automated Negotiation, pp. 211- 224, Springer, 2016.

Type: Workshop

Links: http://link.springer.com/chapter/10.1007%2F978-4-431-54758-7_3#page-1

Bibtex: @InProceedings{Esparcia_HPAAMS_2013, author={Esparcia, Sergio and S{'a}nchez-Anguix, Victor and Aydo{u{g}}an, Reyhan}, editor={Corchado, Juan M. and Bajo, Javier and Kozlak, Jaroslaw and Pawlewski, Pawel and Molina, Jose M. and Julian, Vicente and Silveira, Ricardo Azambuja and Unland, Rainer and Giroux, Sylvain}, title={A Negotiation Approach for Energy-Aware Room Allocation Systems}, booktitle={Highlights on Practical Applications of Agents and Multi-Agent Systems}, year={2013}, publisher={Springer Berlin Heidelberg}, address={Berlin, Heidelberg}, pages={280--291}, abstract={This paper addresses energy-aware room allocation management where the system aims to satisfy individuals' needs as much as possible while concerning total energy consumption in a building. In the problem, there are a several rooms having varied settings resulting in different energy consumption. The main objective of the system is not only finding the right allocations for user's need, but also minimizing energy consumption. However, the users of the system may have conflicting preferences over the rooms to be allocated for them. This paper pursues how the system can increase user satisfaction while achieving its goals. For that purpose, an adaptation of the mediated single text negotiation model is introduced. The proposal seeks to guarantee an upper bound on energy consumption by pruning the negotiation space via a genetic algorithm, and to take advantage of the negotiation for increasing user satisfaction. Experiments suggest that the adaptations improve the performance.}, isbn={978-3-642-38061-7} }

Abstract: When more than two participants have a conflict of interest, finding a mutual agreement may entail a time consuming process especially when the number of participants is high. Automated negotiation tools can play a key role in providing effective solutions. This paper presents two variants of feedback based multilateral negotiation protocol in which a mediator agent generates bids and negotiating agents give their feedback about those bids. We investigate different types of feedback given to the mediator. The mediator uses agents’ feedback to models each agent’s preferences and accordingly generates well-targeted bids over time rather than arbitrary bids. Furthermore, the paper investigates the performance of the protocols in an experimental setting. Experimental results show that the proposed protocols result in a reasonably good outcome for all agents in a relatively short time.

Cite As: Reyhan Aydoğan, Koen Hindriks, and Jonker, Catholijn Jonker, “Multilateral Mediated Negotiation Protocols with Feedback”, In I. Marsa-Maestre, et. al. (editors). Novel Insights in Agent based Complex Automated Negotiation, Studies in Computational Intelligence, p. 43-59, Springer Japan, 2014.

Type: Workshop

Links: http://link.springer.com/chapter/10.1007/978-4-431-54758-7_1

Bibtex: @Inbook{Aydogan_CANMASC_2013, author={Aydo{u{g}}an, Reyhan and Baarslag, Tim and Hindriks, Koen V. and Jonker, Catholijn M. and Yolum, P{i}nar}, editor={Ito, Takayuki and Zhang, Minjie and Robu, Valentin and Fatima, Shaheen and Matsuo, Tokuro}, title={Heuristic-Based Approaches for CP-Nets in Negotiation}, bookTitle={Complex Automated Negotiations: Theories, Models, and Software Competitions}, year={2013}, publisher={Springer Berlin Heidelberg}, address={Berlin, Heidelberg}, pages={113--123}, abstract={CP-Nets have proven to be an effective representation for capturing preferences. However, their use in multiagent negotiation is not straightforward. The main reason for this is that CP-Nets capture partial ordering of preferences, whereas negotiating agents are required to compare any two outcomes based on the request and offers. This makes it necessary for agents to generate total orders from their CP-Nets. We have previously proposed a heuristic to generate total orders from a given CP-Net. This paper proposes another heuristic based on Borda count, applies it in negotiation, and compares its performance with the previous heuristic.}, isbn={978-3-642-30737-9}, doi={10.1007/978-3-642-30737-9_7}, url={https://doi.org/10.1007/978-3-642-30737-9_7} }

Abstract: Under some circumstances, a group of individuals may need to negotiate together as a negotiation team against another party. Unlike bilateral negotiation between two individuals, this type of negotiations entails to adopt an intra-team strategy for negotiation teams in order to make team decisions and accordingly negotiate with the opponent. It is crucial to be able to negotiate successfully with heterogeneous opponents since opponents’ negotiation strategy and behavior may vary in an open environment. While one opponent might collaborate and concede over time, another may not be inclined to concede. This paper analyzes the performance of recently proposed intra-team strategies for negotiation teams against different categories of opponents: competitors, matchers, and conceders. Furthermore, it provides an extension of the negotiation tool Genius for negotiation teams in bilateral settings. Consequently, this work facilitates research in negotiation teams.

Cite As: Victor Sanchez-Anguix, Reyhan Aydoğan, Vincente Julian, and Catholijn Jonker, “Analysis of Intra-Team Strategies for Teams Negotiating Against Competitor, Matchers, and Conceders”, In I. Marsa-Maestre, et al. (editors). Novel Insights in Agent based Complex Automated Negotiation, Studies in Computational Intelligence, p. 3-22, Springer Japan, 2014.

Type: Workshop

Links: http://link.springer.com/chapter/10.1007%2F978-3-642-38061-7_27#page-1

Bibtex: @Inbook{Aydogan_NTIACAN_2012, author={Aydo{u{g}}an, Reyhan and Yolum, P{i}nar}, editor={Ito, Takayuki and Zhang, Minjie and Robu, Valentin and Fatima, Shaheen and Matsuo, Tokuro}, title={The Effect of Preference Representation on Learning Preferences in Negotiation}, bookTitle={New Trends in Agent-Based Complex Automated Negotiations}, year={2012}, publisher={Springer Berlin Heidelberg}, address={Berlin, Heidelberg}, pages={3--20}, abstract={In online and dynamic e-commerce environments, it is beneficial for parties to consider each other's preferences in carrying out transactions. This is especially important when parties are negotiating, since considering preferences will lead to faster closing of deals. However, in general may not be possible to know other participants' preferences. Thus, learning others' preferences from the bids exchanged during the negotiation becomes an important task. To achieve this, the producer agent may need to make assumptions about the consumer's preferences and even its negotiation strategy. Nevertheless, these assumptions may become inconsistent with a variety of preference representations. Therefore, it is more desired to develop a learning algorithm, which is independent from the participants' preference representations and negotiation strategies. This study presents a negotiation framework in which the producer agent learns an approximate model of the consumer's preferences regardless of the consumer's preference representation. For this purpose, we study our previously proposed inductive learning algorithm, namely Revisable Candidate Elimination Algorithm (RCEA). Our experimental results show that a producer agent can learn the consumer's preferences via RCEA when the consumer represents its preferences using constraints or CP-nets. Further, in both cases, learning speeds up the negotiation considerably.}, isbn={978-3-642-24696-8}, doi={10.1007/978-3-642-24696-8_1}, url={https://doi.org/10.1007/978-3-642-24696-8_1} }

Abstract: This paper addresses energy-aware room allocation management where the system aims to satisfy individuals’ needs as much as possible while concerning total energy consumption in a building. In the problem, there are a several rooms having varied settings resulting in different energy consumption. The main objective of the system is not only finding the right allocations for user’s need, but also minimizing energy consumption. However, the users of the system may have conflicting preferences over the rooms to be allocated for them. This paper pursues how the system can increase user satisfaction while achieving its goals. For that purpose, an adaptation of the mediated single text negotiation model is introduced. The proposal seeks to guarantee an upper bound on energy consumption by pruning the negotiation space via a genetic algorithm, and to take advantage of the negotiation for increasing user satisfaction. Experiments suggest that the adaptations improve the performance.

Cite As: Sergio Esparcia, Victor Sanchez-Anguix, and Reyhan Aydogan, “A Negotiation Approach for Energy-aware Room Allocation Systems”, J. M. Corchado, et al. (editors). Highlights on Practical Applications of Agents and Multi-Agent Systems Communications in Computer and Information Science Volume 365, pp. 280-291, 2013.

Type: Workshop

Links: http://link.springer.com/chapter/10.1007%2F978-3-642-30737-9_7#page-1

Bibtex: @InProceedings{Aydogan_Tasdemir_AMECTADA_2010, author={Aydo{u{g}}an, Reyhan and Ta{c{s}}demir, Nuri and Yolum, P{i}nar}, editor={Ketter, Wolfgang and La Poutr{'e}, Han and Sadeh, Norman and Shehory, Onn and Walsh, William}, title={Reasoning and Negotiating with Complex Preferences Using CP-Nets}, booktitle={Agent-Mediated Electronic Commerce and Trading Agent Design and Analysis}, year={2010}, publisher={Springer Berlin Heidelberg}, address={Berlin, Heidelberg}, pages={15--28}, abstract={Automated negotiation is important for carrying out flexible transactions. Agents that take part in automated negotiation need to have a concise representation of their user's preferences and should be able to reason on these preferences effectively. We develop an automated negotiation platform wherein consumer agents negotiate with producer agents about services. A consumer agent represents its user's preferences in a compact way using a CP-net, which is a structure that allows users to order their preferences based on the different value combinations of attributes. Acquiring user's preferences in a compact way is crucial since it significantly decreases the number of questions to be asked to the user by the consumer agent. We design strategies for consumer agents to reason on and negotiate effectively with the preference graph induced from a CP-net. These strategies are designed to generate deals that are acceptable by the provider and the consumer. We compare our proposed strategies in terms of how well and how quickly they can find desirable deals for the consumer.}, isbn={978-3-642-15237-5} }

Abstract: CP-Nets have proven to be an effective representation for capturing preferences. However, their use in multiagent negotiation is not straightforward. The main reason for this is that CP-Nets capture partial ordering of preferences, whereas negotiating agents are required to compare any two outcomes based on the request and offers. This makes it necessary for agents to generate total orders from their CP-Nets. We have previously proposed a heuristic to generate total orders from a given CP-Net. This paper proposes another heuristic based on Borda count, applies it in negotiation, and compares its performance with the previous heuristic.

Cite As: Reyhan Aydoğan, Tim Baarslag, Koen Hindriks, Catholijn M. Jonker, and Pınar Yolum, “Heuristic-based Approaches for CP-nets in Negotiation”, In I. Takayuki et. al. (editors), Complex Automated Negotiations: Theories, Models, and Software Competitions, Vol. 435 of Studies in Computational Intelligence, pp. 113-123. Springer Berlin Heidelberg , 2013.

Type: Workshop

Links: http://link.springer.com/chapter/10.1007%2F978-3-642-24696-8_1#page-1

Bibtex: @Inbook{Aydogan_NTABCAN_2012, author={Aydo{u{g}}an, Reyhan and Yolum, P{i}nar}, editor={Ito, Takayuki and Zhang, Minjie and Robu, Valentin and Fatima, Shaheen and Matsuo, Tokuro}, title={The Effect of Preference Representation on Learning Preferences in Negotiation}, bookTitle={New Trends in Agent-Based Complex Automated Negotiations}, year={2012}, publisher={Springer Berlin Heidelberg}, address={Berlin, Heidelberg}, pages={3--20}, abstract={In online and dynamic e-commerce environments, it is beneficial for parties to consider each other's preferences in carrying out transactions. This is especially important when parties are negotiating, since considering preferences will lead to faster closing of deals. However, in general may not be possible to know other participants' preferences. Thus, learning others' preferences from the bids exchanged during the negotiation becomes an important task. To achieve this, the producer agent may need to make assumptions about the consumer's preferences and even its negotiation strategy. Nevertheless, these assumptions may become inconsistent with a variety of preference representations. Therefore, it is more desired to develop a learning algorithm, which is independent from the participants' preference representations and negotiation strategies. This study presents a negotiation framework in which the producer agent learns an approximate model of the consumer's preferences regardless of the consumer's preference representation. For this purpose, we study our previously proposed inductive learning algorithm, namely Revisable Candidate Elimination Algorithm (RCEA). Our experimental results show that a producer agent can learn the consumer's preferences via RCEA when the consumer represents its preferences using constraints or CP-nets. Further, in both cases, learning speeds up the negotiation considerably.}, isbn={978-3-642-24696-8}, doi={10.1007/978-3-642-24696-8_1}, url={https://doi.org/10.1007/978-3-642-24696-8_1} }

Abstract: In online and dynamic e-commerce environments, it is beneficial for parties to consider each other’s preferences in carrying out transactions. This is especially important when parties are negotiating, since considering preferences will lead to faster closing of deals. However, in general may not be possible to know other participants’ preferences. Thus, learning others’ preferences from the bids exchanged during the negotiation becomes an important task. To achieve this, the producer agent may need to make assumptions about the consumer’s preferences and even its negotiation strategy. Nevertheless, these assumptions may become inconsistent with a variety of preference representations. Therefore, it is more desired to develop a learning algorithm, which is independent from the participants’ preference representations and negotiation strategies. This study presents a negotiation framework in which the producer agent learns an approximate model of the consumer’s preferences regardless of the consumer’s preference representation. For this purpose, we study our previously proposed inductive learning algorithm, namely Revisable Candidate Elimination Algorithm (RCEA). Our experimental results show that a producer agent can learn the consumer’s preferences via RCEA when the consumer represents its preferences using constraints or CP-nets. Further, in both cases, learning speeds up the negotiation considerably.

Cite As: Reyhan Aydoğan and Pınar Yolum, “The Effect of Preference Representation on Learning Preferences in Negotiation”, In T. Ito, M. Zhang, V. Robu, S. Fatima, and T. Matsuo (editors), New Trends in Agent-Based Complex Automated Negotiations. Volume 383 of Studies in Computational Intelligence, pp. 3-20. Springer Berlin Heidelberg, 2012.

Type: Workshop

Links: http://link.springer.com/chapter/10.1007%2F978-3-642-15237-5_2#page-1

Bibtex: @InProceedings{Aydogan_AMECTADA_2010, author={Aydo{u{g}}an, Reyhan and Ta{c{s}}demir, Nuri and Yolum, P{i}nar}, editor={Ketter, Wolfgang and La Poutr{'e}, Han and Sadeh, Norman and Shehory, Onn and Walsh, William}, title={Reasoning and Negotiating with Complex Preferences Using CP-Nets}, booktitle={Agent-Mediated Electronic Commerce and Trading Agent Design and Analysis}, year={2010}, publisher={Springer Berlin Heidelberg}, address={Berlin, Heidelberg}, pages={15--28}, abstract={Automated negotiation is important for carrying out flexible transactions. Agents that take part in automated negotiation need to have a concise representation of their user's preferences and should be able to reason on these preferences effectively. We develop an automated negotiation platform wherein consumer agents negotiate with producer agents about services. A consumer agent represents its user's preferences in a compact way using a CP-net, which is a structure that allows users to order their preferences based on the different value combinations of attributes. Acquiring user's preferences in a compact way is crucial since it significantly decreases the number of questions to be asked to the user by the consumer agent. We design strategies for consumer agents to reason on and negotiate effectively with the preference graph induced from a CP-net. These strategies are designed to generate deals that are acceptable by the provider and the consumer. We compare our proposed strategies in terms of how well and how quickly they can find desirable deals for the consumer.}, isbn={978-3-642-15237-5} }

Abstract: Automated negotiation is important for carrying out flexible transactions. Agents that take part in automated negotiation need to have a concise representation of their user’s preferences and should be able to reason on these preferences effectively. We develop an automated negotiation platform wherein consumer agents negotiate with producer agents about services. A consumer agent represents its user’s preferences in a compact way using a CP-net, which is a structure that allows users to order their preferences based on the different value combinations of attributes. Acquiring user’s preferences in a compact way is crucial since it significantly decreases the number of questions to be asked to the user by the consumer agent. We design strategies for consumer agents to reason on and negotiate effectively with the preference graph induced from a CP-net. These strategies are designed to generate deals that are acceptable by the provider and the consumer. We compare our proposed strategies in terms of how well and how quickly they can find desirable deals for the consumer.

Cite As: Reyhan Aydoğan, Nuri Taşdemir and Pınar Yolum, “Reasoning and Negotiating with Complex Preferences Using CP-nets”, Aalst, W., J. Mylopoulos, N. M. Sadeh, M. J. Shaw, C. Szyperski, W. Ketter, H. Poutr, N. Sadeh, O. Shehory, and W. Walsh (editors), Agent-Mediated Electronic Commerce and Trading Agent Design and Analysis, Vol. 44 of Lecture Notes in Business Information Processing, pp. 15–28, Springer Berlin Heidelberg, 2010.

Type: Workshop

Links: https://ebooks.iospress.nl/publication/51652

Abstract: This paper proposes and evaluates a new airline disruption management policy using agent-based modelling, simulation, and verification. The new policy is based on a multi-agent negotiation protocol and is compared with three airline policies based on established industry practices. The application concerns Airline Operations Control whose core functionality is disruption management. In order to evaluate the new policy, a rule-based agent-based model of the AOC and crew processes has been developed. This model is used to assess the effects of multi-agent negotiation on airline performance in the context of a challenging disruption scenario. For the specific scenario considered, the multi-agent negotiation policy outperforms the established policies when the agents involved in the negotiation are experts. Another important contribution is that the paper presents a logic-based ontology used for formal modelling and analysis of AOC workflows.

Cite As: Soufiane Bouarfa, Reyhan Aydoğan and Alexei Sharpanskykh, “A Multi-Agent Negotiation Approach for Airline Operation Control”, In Proceedings of 8th Workshop on the Reliability of Intelligent Environments, In Series of Ambient Intelligence and Smart Environments, pp. 377-388, 2019.

Type: Other Peer Reviewed

Links: https://people.cs.kuleuven.be/~joost.vennekens/DN/bnaic-proceedings/bnaic2013.pdf

Cite As: Reyhan Aydoğan, Koen Hindriks, and Catholijn Jonker, “Multilateral Mediated Negotiation Protocols with Feedback”, Extended Abstract, In the Proceedings of the 25th Benelux Conference on Artificial Intelligence (BNAIC 2013), Delft, 2013.

Type: Other Peer Reviewed

Links: https://www.scitepress.org/Link.aspx?doi=10.5220/0003718302430248

Cite As: Wietske Visser, Reyhan Aydoğan, Koen Hindriks and Catholijn Jonker, “A Framework for Qualitative Multi-Criteria Preferences: Extended Abstract”, In Proceedings of the 24th Benelux Conference on Artificial Intelligence (BNAIC 2012), pp. 327-328, Maastricht, 2012.

Type: Other Peer Reviewed

Links: https://intelisys.ozyegin.edu.tr/sites/default/files/2021-04/1632-8431-1-PB.pdf

Cite As: Reyhan Aydoğan, “Preferences and Learning in Multi-agent Negotiation”, AAAI-Doctoral Consortium, pp. 1972-1973, Atlanta, USA, 2010.

Type: Other Peer Reviewed

Cite As: Reyhan Aydoğan and Pınar Yolum, “Heuristics for CP-Nets: Negotiating Effectively with Partial Preferences”, In HUCOM Workshop in Group Decision and Negotiation (GDN), pp. 34-40, Delft, the Netherlands, 2010.

Type: Other Peer Reviewed

Links: https://dl.acm.org/doi/10.5555/1402782.1402784

Bibtex: @inproceedings{Aydogan_AMAAS_2008, author = {Aydo{u{g}}an, Reyhan}, title = {Content-oriented composite service negotiation with complex preferences}, year = {2008}, publisher = {International Foundation for Autonomous Agents and Multiagent Systems}, address = {Richland, SC}, abstract = {In e-commerce, for some cases the service requested by the consumer cannot be fulfilled by the producer. In such cases, service consumers and producers need to negotiate their service requirements and offers. Whereas some multiagent negotiation approaches treat the price as the primary construct for negotiation, we consider that the service content is as much important as the price. Therefore, this study mainly focuses on the content of the service described in a common ontology accessed by both agents for common understanding. Acquiring user's preferences and acting upon these preferences are crucial tasks for a consumer agent as far as the negotiation is concerned. Since the size of complete preference information increases exponentially with the number of attributes and size of domain, it is required to keep these preferences in a compact way. There are a variety of ways of representing preferences and using these structures for automatic generation of consumer's request. This research develops an automated negotiation approach in which the consumer takes the preferences of the user in an efficient way and uses these preferences in the generation of request. For this purpose, we design several strategies to generate requests to take the best offer by the producer. On the other side, in order to obtain a more effective negotiation results the producer tries to learn the consumer preferences from the bid exchanges incrementally in order to refine its offer over time. Furthermore, for some complicated services desired by the consumer, a single producer by itself may not meet the consumer's needs. In such cases, the system should allow consumers negotiating with multiple service producers as far as composite services are concerned.}, booktitle = {Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems: Doctoral Mentoring Program}, pages = {1725–1726}, numpages = {2}, location = {Estoril, Portugal}, series = {AAMAS '08} }

Abstract: In e-commerce, for some cases the service requested by the consumer cannot be fulfilled by the producer. In such cases, service consumers and producers need to negotiate their service requirements and offers. Whereas some multiagent negotiation approaches treat the price as the primary construct for negotiation, we consider that the service content is as much important as the price. Therefore, this study mainly focuses on the content of the service described in a common ontology accessed by both agents for common understanding. Acquiring user's preferences and acting upon these preferences are crucial tasks for a consumer agent as far as the negotiation is concerned. Since the size of complete preference information increases exponentially with the number of attributes and size of domain, it is required to keep these preferences in a compact way. There are a variety of ways of representing preferences and using these structures for automatic generation of consumer's request. This research develops an automated negotiation approach in which the consumer takes the preferences of the user in an efficient way and uses these preferences in the generation of request. For this purpose, we design several strategies to generate requests to take the best offer by the producer. On the other side, in order to obtain a more effective negotiation results the producer tries to learn the consumer preferences from the bid exchanges incrementally in order to refine its offer over time. Furthermore, for some complicated services desired by the consumer, a single producer by itself may not meet the consumer's needs. In such cases, the system should allow consumers negotiating with multiple service producers as far as composite services are concerned.

Cite As: Reyhan Aydoğan, “Content-Oriented Composite Service Negotiation with Complex Preferences”, AAMAS-08 Doctoral Mentoring, Estoril, Portugal, 2008.

Type: Other Peer Reviewed

Cite As: Reyhan Aydoğan, “Content-oriented Composite Service Negotiation in E-commerce”, AAMAS 2007 Doctoral Mentoring Program, pp. 8-9, Hawaii, USA, May 2007.

Type: Other Peer Reviewed

Cite As: Reyhan Aydoğan and Pınar Yolum, “Learning Consumer Preferences for Content-Oriented Negotiation”, AAMAS-06 Workshop-Business Agents and the Semantic Web, Japan, May 2006.

Type: Other Peer Reviewed