On Customizing Evolutionary Learning of Agent Behavior

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Abstract

The fitness function of an evolutionary algorithm is one of the few possible spots where application knowledge can be made available to the algorithm. But the representation and use of knowledge in the fitness function is rather indirect and therefore not easy to achieve. In this paper, we present several case studies encoding application specific features into fitness functions for learning cooperative behavior of agents, an application that already requires complex and difficult to manipulate fitness functions. Our experiments with different variants of the Pursuit Game show that refining a knowledge feature already in the fitness function usually does not result in much difference in performance, while adding new application knowledge features to the fitness function improves the learning performance significantly.