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Strategy Acquisition of Agents in Multi-Issue Negotiation
Hong Kong, China December 18-December 22
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/WI.2006.1602006 IEEE/WIC/ACM International Confe ...
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Shohei Yoshikawa, Kobe University, Japan
Takahiko Kamiryo, Kobe University, Japan
Yoshiaki Yasumura, Kobe University, Japan
Kuniaki Uehara, Kobe University, Japan
This paper presents a method for acquiring a strategy of an agent in multi-issue negotiation. This method learns how to make a concession to an opponent for realizing win-win negotiation. To learn the concession strategy, we adopt reinforcement learning. First, an agent receives a proposal from an opponent. The agent recognizes a negotiation state using the difference between their proposals and difference between their concessions. According to the state, the agent makes a proposal by reinforcement learning. A reward of the learning is a profit of an agreement and punishment of negotiation breakdown. The experimental results showed that agents could acquire a negotiation strategy that avoids negotiation breakdown and increases profits of an agreement. As a result, agents can acquire the action policy that strikes a balance between cooperation and competition.
Citation:
Shohei Yoshikawa, Takahiko Kamiryo, Yoshiaki Yasumura, Kuniaki Uehara, "Strategy Acquisition of Agents in Multi-Issue Negotiation," wi, pp.933-939, 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI'06), 2006
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