A Fuzzy Constraint-based Agent Negotiation With Opponent Learning

This work offers a general framework of fuzzy constraint-based agent negotiation with opponent learning. The proposed approach via fuzzy probability constraint clusters the opponent’s information in negotiation process as proximate regularities to increase the efficiency on the convergence of behavior patterns, and eliminates the bulk of false hypotheses or beliefs to improves the effectiveness on beliefs learning. By using fuzzy instance method, our approach can not only reuse the prior opponent knowledge to speed up problem-solving, but also reason the proximate regularities to acquire desirable outcomes on predicting opponent behavior. Besides, the proposed interaction method enables the negotiating agent to adapt dynamically based on expected objectives. Moreover, experimental results suggest that the proposed framework allowed an agent to achieve a higher reward, fairer deal, or less cost of negotiation. Key–Words: Intelligence systems, multi-agent systems, agent negotiation, opponent modeling, beliefs learning, fuzzy constraints.