Emergence of specialized behavior in a pursuit-evasion game

Abstract

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.