Skip to main content


Topic

 Machine Learning, Agent-based Negotiation, Decision Making


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.


Bibtex info
@article{razeghi_deep_2020,
    title = {Deep reinforcement learning for acceptance strategy in bilateral negotiations},
    volume = {28},
    doi = {10.3906/elk-1907-215},
    journal = {TURKISH JOURNAL OF ELECTRICAL ENGINEERING \& COMPUTER SCIENCES},
    author = {Razeghi, Yousef and YAVUZ, Ozan and Aydo\u{g}an, Reyhan},
    year = {2020},
    pages = {1824--1840},
}

Authors
Yousef Razeghi, O. Yavuz, Reyhan Aydogan

Keywords - tags
Automated Bilateral Negotiation, Acceptance Strategy, Deep Reinforcement Learning, DQN

Publication type
Journal Article

Year
2020