Preference Modeling and Reasoning, Opponent Modelling, Decision Making, Agent-based Negotiation, Machine Learning
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.
Bibtex info
@incollection{aydogan_effect_2012,
address = {Berlin, Heidelberg},
title = {The {Effect} of {Preference} {Representation} on {Learning} {Preferences} in {Negotiation}},
isbn = {978-3-642-24696-8},
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.},
booktitle = {New {Trends} in {Agent}-{Based} {Complex} {Automated} {Negotiations}},
publisher = {Springer Berlin Heidelberg},
author = {Aydoğan, Reyhan and Yolum, Pınar},
editor = {Ito, Takayuki and Zhang, Minjie and Robu, Valentin and Fatima, Shaheen and Matsuo, Tokuro},
year = {2012},
doi = {10.1007/978-3-642-24696-8_1},
pages = {3--