Abstract
We present a theoretical framework and an experimental tool to study behavior of heterogeneous multi-agent systems composed of the two classes of automata-based agents: Cellular Automata (CA) and Learning Automata (LA). Our general aim is to use this framework to solve global optimization problems in a distributed way using the collective behavior of agents. The common feature of CA and LA systems is the ability to show a collective behavior which, however, is understood differently. It is natural for LA-based agents that are able to learn and adapt, but for CA-based agents, extra features have to be used like the second–order CA. We create a theoretical framework of the system based on a spatial Prisoner’s Dilemma (PD) game in which both classes of players may participate. We introduce to the game some mechanisms like local profit sharing, mutation, and competition which stimulate the evolutionary process of developing collective behavior among players. We present some results of an experimental study showing the emergence of collective behavior in such systems.
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The authors wish to thank the student of UKSW Dominik Nalewajk for implementation of the simulator.
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Seredyński, F., Gąsior, J., Hoffmann, R., Désérable, D. (2020). Experiments with Heterogenous Automata-Based Multi-agent Systems. In: Wyrzykowski, R., Deelman, E., Dongarra, J., Karczewski, K. (eds) Parallel Processing and Applied Mathematics. PPAM 2019. Lecture Notes in Computer Science(), vol 12044. Springer, Cham. https://doi.org/10.1007/978-3-030-43222-5_38
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