Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Emergence of collective strategies in a prey-predator game model
Artificial Life
RoboCop: today and tomorrow-what we have learned
Artificial Intelligence - Special issue on Robocop: the first step
Mobile Robot Miniaturisation: A Tool for Investigation in Control Algorithms
The 3rd International Symposium on Experimental Robotics III
Evolving Beharioral Strategies in Predators and Prey
IJCAI '95 Proceedings of the Workshop on Adaption and Learning in Multi-Agent Systems
COOPERATIVE COEVOLUTION OF MULTI-AGENT SYSTEMS
COOPERATIVE COEVOLUTION OF MULTI-AGENT SYSTEMS
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This research concerns the comparison of three different artificial evolution approaches to the design of cooperative behavior in a group of simulated mobile robots. The first and second approaches, termed: single pool and plasticity, are characterized by robots that share a single genotype, though the plasticity approach includes a learning mechanism. The third approach, termed: multiple pools, is characterized by robots that use different genotypes. The application domain implements a pursuit-evasion game in which teams of robots of various sizes, termed: predators, collectively work to capture either one or two others, termed: prey. These artificial evolution approaches are also compared with a static rule based cooperative pursuit strategy specified a priori. Results indicate that the multiple pools approach is superior comparative to the other approaches in terms of measures defined for prey-capture strategy performance. That is, this approach facilitated specialization of behavioral roles allowing it to be effective for all predator team sizes tested.