Recognizing players' activities and hidden state

Authors:
Wesley Kerr;Paul R. Cohen;Niall Adams
Affiliations:
University of Arizona, Tucson, Arizona;University of Arizona, Tucson, Arizona;Imperial College, London, England
Venue:
Proceedings of the 6th International Conference on Foundations of Digital Games
Year:
2011

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Abstract

This paper describes a machine learning approach to classifying the activities of players in games. Instances of activities generally are not identical because they play out in different contexts, so the challenge is to extract the "essences" of activities from instances. We show how this problem may be mapped to a sequence alignment problem, for which there are polynomial-time solutions. The method works well even when some features of activities are not observable (e.g., the emotional states of players). In fact, these features can in some conditions be inferred with high accuracy.