Strategy acquisition on multi-issue negotiation without estimating opponent's preference

  • Authors:

  • Shohei Yoshikawa;Yoshiaki Yasumura;Kuniaki Uehara

  • Affiliations:

  • Dept. of Computer Science and Systems Engineering, Graduate School of Engineering, Kobe University, Japan;Dept. of Computer Science and Systems Engineering, Graduate School of Engineering, Kobe University, Japan;Dept. of Computer Science and Systems Engineering, Graduate School of Engineering, Kobe University, Japan

  • Venue:
  • KES-AMSTA'08 Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications
  • Year:
  • 2008

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Abstract

In multi-issue negotiation, an opponent's preference is rarely open. Under this environment, it is difficult to acquire a negotiation result that realizes win-win negotiation. In this paper, we present a novel method for realizing win-win negotiation although an opponent's preference is not open. In this method, an agent learns how to make a concession to an opponent. To learn the concession strategy, we adopt reinforcement learning. In reinforcement learning, the agent recognizes a negotiation state to each issue in negotiation. According to the state, the agent makes a proposal to increase own profit. A reward of the learning is a profit of an agreement and punishment of negotiation breakdown. Experimental results showed that agents could acquire a negotiation strategy that avoids negotiation breakdown and increases profits of both sides. Finally, the agents can acquire the action policy that strikes a balance between cooperation and competition.