Strategy Acquisition of Agents in Multi-Issue Negotiation

  • Authors:

  • Shohei Yoshikawa;Takahiko Kamiryo;Yoshiaki Yasumura;Kuniaki Uehara

  • Affiliations:

  • Kobe University, Japan;Kobe University, Japan;Kobe University, Japan;Kobe University, Japan

  • Venue:
  • WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
  • Year:
  • 2006

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Abstract

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.