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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.

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This study conducts a novel user profile learning approach based on fuzzy constraints. From the vantage of knowledge representation, a fuzzy constraint network is used not only to present the ambiguity of concepts and the diversity between concepts but also to express a single user profile with dependent multisubjects of interest. From the vantage of problem solving, the construction of a user profile is viewed as a problem of fuzzy constraint satisfaction. The subject of interest is extracted by a spreading activation model. To achieve the information filtering of the retrieved data, fuzzy information gain is employed to reduce unnecessary user feedback for matching the user's retrieval requirements.