Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
Self-Organization in Biological Systems
Self-Organization in Biological Systems
Evolving neural networks through augmenting topologies
Evolutionary Computation
The Legion System: A Novel Approach to Evolving Hetrogeneity for Collective Problem Solving
Proceedings of the European Conference on Genetic Programming
COOPERATIVE COEVOLUTION OF MULTI-AGENT SYSTEMS
COOPERATIVE COEVOLUTION OF MULTI-AGENT SYSTEMS
Evolving Self-Organizing Behaviors for a Swarm-Bot
Autonomous Robots
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Generative encoding for multiagent learning
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Computer
Distributed Consensus in Multi-vehicle Cooperative Control: Theory and Applications
Distributed Consensus in Multi-vehicle Cooperative Control: Theory and Applications
The sensitivity of HyperNEAT to different geometric representations of a problem
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Genetic team composition and level of selection in the evolution of cooperation
IEEE Transactions on Evolutionary Computation
Evolving policy geometry for scalable multiagent learning
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Real-time neuroevolution in the NERO video game
IEEE Transactions on Evolutionary Computation
Encouraging reactivity to create robust machines
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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This paper describes a study of the evolution of distributed behavior, specifically the control of agents in a mobile ad hoc network, using neuroevolution. In neuroevolution, a population of artificial neural networks (ANNs) are subject to mutation and natural selection. For this study, we compare three different neuroevolutionary systems: a direct encoding, an indirect encoding, and an indirect encoding that supports heterogeneity. Multiple variations of each of these systems were tested on a problem where agents were able to coordinate their collective behavior. Specifically, movement of agents in a simulated physics environment affected which agents were able to communicate with each other. The results of experiments indicate that this is a challenging problem domain for neuroevolution, and although direct and indirect encodings tended to perform similarly in our tests, the strategies employed by indirect encodings tended to favor stable, cohesive groups, while the direct encoding versions appeared more stochastic in nature.