Abstract
This work adopted the fuzzy constraint-directed approach to model opponent’s beliefs in agent negotiation. The fuzzy constraint-directed approach involves the fuzzy probability constraint and the fuzzy instance reasoning. The fuzzy probability constraint is used to cluster the opponent’s regularities and to eliminate the noisy hypotheses or beliefs, so as to increase the efficiency on the convergence of behavior patterns and to improve the effectiveness on beliefs learning. The fuzzy instance reasoning reuses 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 allows the agent to make a concession dynamically based on desirable objectives. Moreover, experimental results suggest that the proposed framework enabled an agent to achieve a higher reward, a fairer deal, or a less cost of negotiation.
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References
Bowling, M., Veloso, M.: Multiagent Learning Using a Variable Learning Rate. Artificial Intelligence 33, 215–250 (2002)
Cai, K.Y.: Robustness of Fuzzy Reasoning and Equalities of Fuzzy Sets. IEEE Transactions on Fuzzy Systems 9(5), 138–150 (2001)
Carmel, D., Markovitch, S.: Model-Based Learning of Interaction Strategies in Multi-Agent Systems. Journal of Experimental and Theoretical Artificial Intelligence 10, 309–332 (1998)
Dubois, D., Fragier, H., Prade, H.: Propagation and Satisfaction of Flexible Constraints. In: Yager, R., Zadeh, L. (eds.) Fuzzy Sets, Neural Networks and Soft Computing, Van Nostrand Reinhold, New York, pp. 166–187 (1994)
Dubois, D., Fortemps, P.: Theory and Methodology Computing Improved Optimal Solutions to Max-Min Flexible Constrain Satisfaction. European Journal of Operational Research 118, 95–126 (1999)
Dubois, D., Fragier, H., Fortemps, P.: Fuzzy Scheduling: Modeling Flexible Constraints vs. Coping with Incomplete Knowledge. European Journal of Operational Research. 147, 231–252 (2003)
Faratin, P., Sierra, C., Jennings, N.R.: Using Similarity Criteria to Make Trade-offs in Automated Negotiation. Artificial Intelligence. 142(2), 205–237 (2002)
Hu, J., Weliman, M.P.: Learning about Other Agents in a Dynamic Multiagent System. Journal of Cognitive Systems Research 2, 67–79 (2001)
Kowalczyk, R., Bui, V.: On Constraint-based Reasoning in R-negotiation Agents. Agent-Mediated Electronic Commerce III. Current Issues in Agent-Based Electronic Commerce Systems, 31–46 (2001)
Lai, K.R.: Fuzzy Constraint Processing. Ph.D. thesis, NCSU, Raleigh, N.C (1992)
Lai, K.R.: Lin, Menq-Wen: Modeling Agent Negotiation via Fuzzy Constraints in E-business. Computational Intelligence 20, 624–642 (2004)
Lai, K.R., Lin, M.-W., Yu, T.-J.: Fuzzy Constraint-Based Agent Negotiation. Journal of Computer Science and Technology 20(3), 319–330 (2005)
Liu, S.H., Tian, Y.T.: Multi-agent Learning Methods in an Uncertain Environment. In: Proc. International Conference on Machine Learning and Cybernetics, 4-5 November, vol. 2, pp. 650–654 (2002)
Luo, X., Leung, H.F., Lee, J.H.M.: A Multi-agent Framework for Meeting Scheduling using Fuzzy Constraints. In: Proc. the Fourth International Conference on MultiAgent Systems, pp. 409–410 (2000)
Luo, X., Jennings, N.R., Shadbolt, N., Leung, H.F., Lee, J.H.M.: A Fuzzy Constraint Based Model for Bilateral Multi-issue Negotiations in Semi-competitive Environments. Artificial Intelligence 148, 53–102 (2003)
Markovitch, S.: Learning and Exploiting Relative Weaknesses of Opponent Agents. NWO-SIKS Workshop on Opponent Models in Games. IKAT, Universiteit Maastricht (December 4, 2003)
Prade, J., Moura-Pires, H.: Specifying Fuzzy Constraints Interactions without using Aggregation Operators. In: Proc. the Ninth IEEE International Conference on Fuzzy Systems, vol. 1, pp. 228–233 (2000)
Pruitt, D.G.: Negotiation Behavior. Academic Press, London (1981)
Ren, Z., Anumba, C.J., Ugwu, O.O.: Negotiation in a Multi-agent System for Construction Claims Negotiation. Applied Artificial Intelligence 16, 359–394 (2002)
Zadeh, L.A.: Fuzzy Sets as A Basis for A Theory of Possibility. Fuzzy Sets and Systems 1, 3–28 (1978)
Zeng, D., Sycara, K.: Bayesian Learning in Negotiation. Internet, J. Human-Computer Stud. 48(1), 125–141 (1998)
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Yu, TJ., Lai, K.R., Lin, MW., Kao, BR. (2007). Modeling Opponent’s Beliefs Via Fuzzy Constraint-Directed Approach in Agent Negotiation. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2007. Lecture Notes in Computer Science, vol 4681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74171-8_17
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DOI: https://doi.org/10.1007/978-3-540-74171-8_17
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