In e-business, disputes between two or more parties arise for various reasons and involve different issues. Thus, resolution of these disputes frequently relies on some form of negotiation. This article presents a general problemsolving framework for modeling multi-issue multilateral agent negotiation using fuzzy constraints in e-business. Fuzzy constraints are thus used not only to define each agent's demands involving human concepts, but also to represent the relationships among agents. A concession strategy, based on fuzzy constraint-based problem-solving, is proposed to relax demands, and a trade-off strategy is presented to evaluate existing alternatives. This approach provides a systematic method for reaching an agreement that benefits all agents with a high satisfaction degree of constraints. Meanwhile, by applying the method, agents can move toward an agreement more quickly, because their search focuses only on the feasible solution space. An example application to negotiate an insurance policy among agents is provided to demonstrate the usefulness and effectiveness of the proposed framework.

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This paper proposes a general problem-solving *amwork for modeling of agent negotiation via l z z y Constraint Processing to acquire an optimal solution that satisfles agents in the environment of incomplete and imprecise information. I n this approach, fuzzy eonstraint processing serves not only to improve the efficiency of the agent's individual negotiation strategy which aims at the maximal satisfaction of its selfinterest, b u t also to represent a negotiation protocol which determines legal or meaningful sequences of messages that must be satisfied. Thus, our approach is modeling agent negotiation based on fuzzy constraint processing as a distributed fuzzy constraint satisfaction problem (DFCSP) which objective is to find a solution that has maximal satisfaction for ali fuzzy constraints in the distributed environment. In this paper, we shall introduce the concept of fuzzy constraint processing and t h e definition of distributed fuzzy constraint network.Then, the approach for modeling agent negotiation via fuzzy constraint processing will be presented. Next, to demonstrate the effectiveness of this approach, an example of phone-trading i n electronic marketplace is provided.

This work presents a general framework of agent negotiation with autonomous learning via fuzzy constraint-directed approach. The fuzzy constraint-directed approach involves the fuzzy probability constraint where each fuzzy constraint has a certain probability, and the fuzzy instance reasoning where each instance is represented as a primitive fuzzy constraint network. The proposed approach via fuzzy probability constraint can not only cluster the opponent's information in negotiation process as proximate regularities to increase the efficiency on the convergence of behavior patterns, but also eliminate the bulk of false hypotheses or beliefs to improves the effectiveness on beliefs learning. By using fuzzy instance method, our approach can reuse the prior opponent knowledge to speed up problem-solving, and reason the proximate regularities to acquire desirable results on predicting opponent behavior. Besides, the proposed interaction method enables the agent to make a concession 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.