Abstract
Approaches which tackle coordination in multi-agent systems have mostly taken communication for granted. In societies of individually-motivated agents where the communication costs are prohibitive, there should be other mechanisms to allow them to coordinate when interacting. In this paper game theory is used as a mathematical tool for modelling the interactions among agents and as a mechanism for coordination with less communication. However we want to loose the assumption that agents are always rational. In the approach discussed here, agents learn how to coordinate by playing repeatedly with neighbors. The dynamics of the interaction is modelled by means of genetic operators. In this way, the behavior of agents as well as the equilibrium of the system can adapt to major external perturbations. If the interaction lasts long enough, then agents can asymptotically learn new coordination points.
the author is supported by Conselho Nacional de Pesquisa Cientifica e Tecnológica — CNPq — Brasil.
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Bazzan, A.L.C. (1996). Coordination among individually-motivated agents: An evolutionary approach. In: Borges, D.L., Kaestner, C.A.A. (eds) Advances in Artificial Intelligence. SBIA 1996. Lecture Notes in Computer Science, vol 1159. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61859-7_8
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DOI: https://doi.org/10.1007/3-540-61859-7_8
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