Abstract
In certain tasks such as pursuit and evasion, multiple agents need
to coordinate their behavior to achieve a common goal. An interesting
question is, how can such behavior best be evolved? When the agents
are controlled with neural networks, a powerful method is to coevolve
them in separate subpopulations, and test together in the common
task. In this paper, such a method, called Multi-Agent ESP (Enforced
Subpopulations) is presented, and demonstrated in a prey-capture
task. The approach is shown more efficient and robust than evolving
a single central controller for all agents. The role of communication
in such domains is also studied, and shown to be unnecessary and
even detrimental if effective behavior in the task can be expressed
as role-based cooperation rather than synchronization.
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