Abstract
The pursuit of a viable model of player behaviour has gained momentum in research in recent years, and it is beginning to attract the attention of the designers of next-generation digital games. In this paper, we present a novel enhancement to player modelling that is well-suited to the digital role-playing and puzzle game industries, titled Goal-Directed- Player Modelling, in which state abstraction based on a player's goals is used to improve the performance of a classifier for predicting player actions. We survey a set of related research, formally introduce a method for Goal-Directed-Player Modelling, and present empirical results which clearly show the ability of Goal-Directed-Player Modelling to greatly improve the accuracy of a simple, online, low cost incremental classi ier to a level near those of more advanced and complex of line methods.</p>
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