Abstract
This paper introduces the learning mechanism by which agents can identify, through experience, important messages in the context of inference in a specific situation. At first, agents may not be able to immediately read and process important messages because of inappropriate ratings, incomplete non-local information, or insufficient knowledge for coordinated actions. By analyzing the history of past inferences with other agents, however, they can identify which messages were really used. Agents then generate situation-specific rules for understanding important messages when a similar problem-solving context appears. This paper also gives an example for explaining how agents can generate the control rule.
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References
Cockburn, D., Jennings, N.R.: ARCHON: A Distributed Artificial Intelligence System for Industrial Applications. In: O’Hare, G.M.P., Jennings, N.R. (eds.) Foundations of Distributed Artificial Intelligence, pp. 319–344.
Decker, K., Lesser, V.R.: QuantitativeModeling ofComplex Environments. International Journal of Intelligent Systems in Accounting, Finance and Management, special issue on Mathematical and Computational Models of Organizations: Models and Characteristics of Agent Behavior (1993)
Decker, K.S., Lesser, V.R.: Designing a Family of Coordination Algorithms. In: Proceedings of the First International Conference on Multiagent Systems, June, AAAI Press, San Francisco (1995)
Hammond, K.J.: Case-Based Planning: Viewing Planning as a Memory Task. Academic Press, London (1989)
Hudlická, E., Lesser, V.R.: Meta-Level Control Through FaultDetection and Diagnosis. In: Proceedings of the 1984 National Conference on AI, pp. 153–161 (1984)
Hudlická, E., Lesser, V.R.: Modeling and Diagnosing Problem-Solving System Behavior. IEEE Transactions on Systems, Man, and Cybernetics 17(3), 407–419 (1987); Bond, A., Gasser, L., (eds.) Also published in Readings in DistributedArtificial Intelligence, pp. 490-502. Morgan Kaufmann Publishers, California (1988)
Lesser, V.R.: A Retrospective View of FA/C Distributed Problem Solving. IEEE Transactions on Systems, Man, and Cybernetics 21(6), 1347–1362 (1991)
Sugawara, T.: A Cooperative LAN Diagnostic and Observation Expert System. In: Proc. Of IEEE Phoenix Conf. on Comp. and Comm., pp. 667–674 (1990)
Sugawara, T., Murakami, K.: A Multiagent Diagnostic System for Internetwork Problems. In: Proc. of INET 1992, Kobe, Japan (1992)
Sugawara, T., Lesser, V.R.: On-Line Learning of Coordination Plans. COINS Technical Report, 93-27, Univ. of Massachusetts (1993); The shorter version of this paper is also published in Proc. of the 12th Int. Workshop on Distributed AI (1993)
Tan, M.: Multi-agent reinforcement learning: Independent vs. cooperative agents. In: Proceedings of the Tenth International Conference on Machine Learning, pp. 330–337 (1993)
Weiss, G.: Some studies in distributedmachine learning and organizational design, Technical Report FKI-189-94, Institut fur Informatik, TU Munchen (1994)
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© 1998 Springer-Verlag Berlin Heidelberg
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Sugawara, T., Kurihara, S. (1998). Learning Message-Related Coordination Control in Multiagent Systems. In: Zhang, C., Lukose, D. (eds) Multi-Agent Systems. Theories, Languages and Applications. DAI 1998. Lecture Notes in Computer Science(), vol 1544. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10693067_3
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DOI: https://doi.org/10.1007/10693067_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65477-3
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