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

Ben Cowley, Darryl Charles, Michaela Black, Raymond Hickey

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.
Original languageEnglish
Title of host publicationUnknown Host Publication
Pages170-177
Number of pages8
Publication statusPublished - Oct 2009
EventIEEE Symposium on Computational Intelligence and Games 2009 - Politecnico di Milano, Milan, Italy
Duration: 1 Oct 2009 → …

Conference

ConferenceIEEE Symposium on Computational Intelligence and Games 2009
Period1/10/09 → …

Bibliographical note

Reference text: [1] B. Cowley, D. Charles, M.M. Black, and R.J. Hickey, �“Using
Decision theory for Player Analysis in Pacman,�” Proceedings of the
SAB Workshop on Adaptive Approaches to Optimizing Player
Satisfaction, Roma, Italy: 2006, pp. 41-50.
[2] B. Cowley, D. Charles, M.M. Black, and R.J. Hickey, �“Data-Driven
Decision theory for Player Analysis in Pacman,�” Proceedings of the
Optimizing Player Satisfaction Workshop, Stanford University,
Stanford, Ca: AAAI Press, 2007, pp. 25-30.
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