Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
A Roadmap of Agent Research and Development
Autonomous Agents and Multi-Agent Systems
Strongly Typed Genetic Programming in Evolving Cooperation Strategies
Proceedings of the 6th International Conference on Genetic Algorithms
Methods for Competitive Co-Evolution: Finding Opponents Worth Beating
Proceedings of the 6th International Conference on Genetic Algorithms
A Coevolutionary Approach to Learning Sequential Decision Rules
Proceedings of the 6th International Conference on Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Emergent Cooperation for Multiple Agents Using Genetic Programming
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Evolving Beharioral Strategies in Predators and Prey
IJCAI '95 Proceedings of the Workshop on Adaption and Learning in Multi-Agent Systems
Evolutionary Computing in Multi-agent Environments: Operators
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Social dilemmas in computational ecosystems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Efficient evaluation functions for evolving coordination
Evolutionary Computation
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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.