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.
Original language | English |
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Pages (from-to) | 1824-1840 |
Number of pages | 17 |
Journal | Turkish Journal of Electrical Engineering and Computer Sciences |
Volume | 28 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2020 |
Keywords
- Acceptance strategy
- Automated bilateral negotiation
- Deep reinforcement learning