Abstract
Multiagent systems in which agents interact with each other are now being proposed as a solution to many problems which can be grouped together under the “distributed problem solving” umbrella. For such systems to work properly, it is necessary that agents learn from their environment and adapt their behaviour accordingly. In this paper we present a system which uses a combination of neuro-fuzzy learning and static adaptation to coordinate the activity of multiple agents. An epistemic utility based formulation is used to automatically generate the exemplars for learning, making the process unsupervised. The system has been developed in the context of a scientific computing scenario.
This work was supported in part by NSF awards ASC 9404859 and CCR 9202536, AFOSR award F49620-92-J-0069 and ARPA ARO award DAAH04-94-G-0010
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© 1996 Springer-Verlag Berlin Heidelberg
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Joshi, A. (1996). To learn or not to learn ....... In: Weiß, G., Sen, S. (eds) Adaption and Learning in Multi-Agent Systems. IJCAI 1995. Lecture Notes in Computer Science, vol 1042. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60923-7_23
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DOI: https://doi.org/10.1007/3-540-60923-7_23
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