Fuzzy constraint processing
Bayesian learning in negotiation
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
Multiagent learning using a variable learning rate
Artificial Intelligence
A Multi-Agent Framework for Meeting Scheduling Using Fuzzy Constraints
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
Fuzzy constraint-based agent negotiation
Journal of Computer Science and Technology
Opponent learning for multi-agent system simulation
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
An overview of cooperative and competitive multiagent learning
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
Robustness of fuzzy reasoning and δ-equalities of fuzzy sets
IEEE Transactions on Fuzzy Systems
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Cognitive Systems Research
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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.