Multiagent compromise via negotiation
Distributed Artificial Intelligence (Vol. 2)
Constraint-directed negotiation of resource reallocations
Distributed Artificial Intelligence (Vol. 2)
Fuzzy constraint processing
Rules of encounter: designing conventions for automated negotiation among computers
Rules of encounter: designing conventions for automated negotiation among computers
Bayesian learning in negotiation
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
A multi-attribute utility theoretic negotiation architecture for electronic commerce
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Multiagent learning using a variable learning rate
Artificial Intelligence
On Constraint-Based Reasoning in e-Negotiation Agents
Agent-Mediated Electronic Commerce III, Current Issues in Agent-Based Electronic Commerce Systems (includes revised papers from AMEC 2000 Workshop)
On Artificial Agents for Negotiation in Electronic Commerce
HICSS '96 Proceedings of the 29th Hawaii International Conference on System Sciences Volume 4: Organizational Systems and Technology
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
Robustness of fuzzy reasoning and δ-equalities of fuzzy sets
IEEE Transactions on Fuzzy Systems
Learning about other agents in a dynamic multiagent system
Cognitive Systems Research
Agent-based Cloud service composition
Applied Intelligence
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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.