Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Mobile Robot Miniaturisation: A Tool for Investigation in Control Algorithms
The 3rd International Symposium on Experimental Robotics III
COOPERATIVE COEVOLUTION OF MULTI-AGENT SYSTEMS
COOPERATIVE COEVOLUTION OF MULTI-AGENT SYSTEMS
Emergence of collective strategies in a prey-predator game model
Artificial Life
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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 robot controllers that share a single genotype, though the plasticity approach includes a learning mechanism. The third approach, termed: multiple pools, is characterized by robot controllers that use different genotypes. The application domain implements a pursuit-evasion game in which a team of robots, termed: pursuers, collectively work to capture one or more robots from a second team, termed: evaders. Results indicate that the multiple pools approach is superior comparative to the other two approaches in terms of measures defined for evader-capture strategy performance. Specifically, this approach facilitates behavioural specialization in the pursuer team allowing it to be effective for several different pursuer team sizes.