• DocumentCode
    3231267
  • Title

    Strategy Acquisition of Agents in Multi-Issue Negotiation

  • Author

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

  • Author_Institution
    Graduate Sch. of Sci. & Technol., Kobe Univ.
  • fYear
    2006
  • fDate
    18-22 Dec. 2006
  • Firstpage
    933
  • Lastpage
    939
  • 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
  • Keywords
    learning (artificial intelligence); multi-agent systems; concession strategy; multiissue negotiation agents; reinforcement learning; Electric breakdown; Equations; Intelligent agent; Internet; Learning; Proposals;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Web Intelligence, 2006. WI 2006. IEEE/WIC/ACM International Conference on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    0-7695-2747-7
  • Type

    conf

  • DOI
    10.1109/WI.2006.160
  • Filename
    4061498