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Strategy Acquisition on Multi-issue Negotiation without Estimating Opponent’s Preference

  • Shohei Yoshikawa
  • Yoshiaki Yasumura
  • Kuniaki Uehara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4953)

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.

Keywords

Multiagent System Negotiation Strategy Linear Strategy Learning Agent Reservation Utility 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Shohei Yoshikawa
    • 1
  • Yoshiaki Yasumura
    • 1
  • Kuniaki Uehara
    • 1
  1. 1.Dept.of Computer Science and Systems Engineering Graduate School of EngineeringKobe UniversityJapan

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