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.
RAZEGHI, YOUSEF; YAVUZ, CELAL OZAN BERK; and AYDOĞAN, REYHAN
"Deep reinforcement learning for acceptance strategy in bilateral negotiations,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 28:
4, Article 2.
Available at: https://journals.tubitak.gov.tr/elektrik/vol28/iss4/2