Evolving multi-modal behavior in NPCs

Authors:
Jacob Schrum;Risto Miikkulainen
Affiliations:
Department of Computer Sciences, University of Texas at Austin, Austin, TX;Department of Computer Sciences, University of Texas at Austin, Austin, TX
Venue:
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Year:
2009

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Abstract

Evolution is often successful in generating complex behaviors, but evolving agents that exhibit distinctly different modes of behavior under different circumstances (multimodal behavior) is both difficult and time consuming. This paper presents a method for encouraging the evolution of multimodal behavior in agents controlled by artificial neural networks: A network mutation is introduced that adds enough output nodes to the network to create a new output mode. Each output mode completely defines the behavior of the network, but only one mode is chosen at any one time, based on the output values of preference nodes. With such structure, networks are able to produce appropriate outputs for several modes of behavior simultaneously, and arbitrate between them using preference nodes. This mutation makes it easier to discover interesting multi-modal behaviors in the course of neuroevolution.