Analyzing player behavior in Pacman using feature-driven decision theoretic predictive modeling

Abstract

We describe the results of a modeling methodology that combines the formal choice-system representation of decision theory with a human player-focused description of the behavioral features of game play in Pacman. This predictive player modeler addresses issues raised in previous work [1] and [2], to produce reliable accuracy. This paper focuses on using player-centric knowledge to reason about player behavior, utilizing a set of features which describe game-play to obtain quantitative data corresponding to qualitative behavioral concepts.