Evolving Multiagent Coordination Strategieswith Genetic

The design and development of behavioral strategies to coordinate the actions of multiple agents is a central issue in multiagent systems research. We propose a novel approach of evolving, rather than handcrafting, behavioral strategies. The evolution scheme used is a variant of the Genetic Programming (GP) paradigm. As a proof of principle, we evolve behavioral strategies in the predator{prey domain that has been studied widely in the Distributed Artiicial Intelligence community. We use the GP to evolve behavioral strategies for individual agents, as prior literature claims that communication between predators is not necessary for successfully capturing the prey. The evolved strategy, when used by each predator, performs better than all but one of the handcrafted strategies mentioned in literature. We analyze the shortcomings of each of these strategies. The next set of experiments involve co{evolving predators and prey. To our surprise, a simple prey strategy evolves that consistently evades all of the predator strategies. We analyze the implications of the relative successes of evolution in the two sets of experiments and comment on the nature of domains for which GP based evolution is a viable mechanism for generating coordination strategies. We conclude with our design for concurrent evolution of multiple agent strategies in domains where agents need to communicate with each other to successfully solve a common problem.