Abstract
Social behaviour in intelligent agent systems is often considered to be achieved by deliberative, in-depth reasoning techniques. This paper shows, how a purely reactive multi-agent system can learn to evolve cooperative behaviour, by means of learning from previous experiences. In particular, we describe a learning multi agent approach to the problem of controlling power flow in an electrical power-grid. The problem is formulated within the framework of dynamic programming. Via a global optimization goal, a set of individual agents is forced to autonomously learn to cooperate and communicate. The ability of the purely reactive distributed systems to solve the global problem by means of establishing a communication mechanism is shown on two prototypical network configurations.
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Riedmiller, M., Moore, A., Schneider, J. (2001). Reinforcement Learning for Cooperating and Communicating Reactive Agents in Electrical Power Grids. In: Balancing Reactivity and Social Deliberation in Multi-Agent Systems. BRSDMAS 2000. Lecture Notes in Computer Science(), vol 2103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44568-4_9
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DOI: https://doi.org/10.1007/3-540-44568-4_9
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