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Learning opponent’s beliefs via fuzzy constraint-directed approach to make effective agent negotiation

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Abstract

This work presents a general framework of agent negotiation with opponent learning via fuzzy constraint-directed approach. The fuzzy constraint-directed approach involves the fuzzy probability constraint and the fuzzy instance reasoning. The proposed approach via fuzzy probability constraint can not only cluster the opponent’s information in negotiation process as proximate regularities to improve the convergence of behavior patterns, but also eliminate the noisy hypotheses or beliefs to enhance the effectiveness on beliefs learning. By using fuzzy instance method, our approach can reuse the prior opponent knowledge to speed up the problem-solving, and reason the proximate regularities to acquire desirable results on predicting opponent behavior. In addition, the proposed interaction method enables the agent to make a concession dynamically based on expected objectives. Moreover, experimental results suggest that the proposed framework allows an agent to achieve a higher reward, a fairer deal, or a smaller cost of negotiation.

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Authors and Affiliations

  1. Yuan Ze University, 135 Yuan-Tung Road, Chung-Li, Taiwan, 32026, ROC

    K. Robert Lai & Ting Jung Yu

  2. Ching Yun University, 229 Chien-Hsin Road, Chung-Li, Taiwan, 32026, ROC

    Menq Wen Lin

Authors
  1. K. Robert Lai
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  2. Menq Wen Lin
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  3. Ting Jung Yu
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Correspondence to Ting Jung Yu.

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Lai, K.R., Lin, M.W. & Yu, T.J. Learning opponent’s beliefs via fuzzy constraint-directed approach to make effective agent negotiation. Appl Intell 33, 232–246 (2010). https://doi.org/10.1007/s10489-009-0162-2

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  • DOI: https://doi.org/10.1007/s10489-009-0162-2

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