Statistical reasoning strategies in the pursuit and evasion domain

Abstract

Isaacs’ treatise on differential games was a break-through for the analysis of the pursuit-and-evasion (PE) domain within the context of strategies representable by differential equations. Current experimental work in Artificial Life steps outside of the formalism of differential games, but the formalism it steps into is yet to be identified. We introduce a formulation of PE that allows a formalism to be developed. Our game minimizes kinematic factors and instead emphasizes the informational aspect of the domain. We use information-theoretic tools to describe agent behavior and implement a pursuit strategy based on statistical decision making; evaders evolved against this pursuit strategy exhibit a wide range of sophisticated behavior that can be quantitatively described. Agent performance is related to these quantifiables.