Abstract
In recent years, considerable interest and enthusiasm have been generated by the prospect of widespread use of intelligent agent-based systems [18]. In particular, a number of researchers have been investigating the design and implementation of systems consisting of multiple agents [26]. The design of successful multiagent systems is, however, a problem of significant magnitude and difficulty. Often multiple, conflicting criteria have to be simultaneously optimized to come up with a cost-effective multiagent system design. Agent system design may involve designing the infrastructure or environment for agent interaction as well as behavioral strategies for individual or multiple agents. Agent behaviors handcrafted offline can be inadequate if possible interactions are overlooked. Genetic algorithms provide us with another tool for designing both individual agent behaviors as well as social rules for multiagent systems. In this chapter we identify different modes for evolving agent groups and present instances of two different approaches: a coevolutionary optimization approach, and an adaptive system approach.
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Sen, S., Debnath, S., Mundhe, M. (2003). Evolving Coordinated Agents. In: Ghosh, A., Tsutsui, S. (eds) Advances in Evolutionary Computing. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18965-4_22
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DOI: https://doi.org/10.1007/978-3-642-18965-4_22
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