Machine Learning, Agent-based Negotiation, Decision Making
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},
}