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Author Thue, David ♦ Bulitko, Vadim
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Player Goal ♦ Present Empirical Result ♦ Low Cost Incremental Classifier ♦ State Abstraction ♦ Digital Roleplaying ♦ Digital Game ♦ Player Behaviour ♦ Goal-directed-player Modelling ♦ Puzzle Game Industry ♦ Goal-directed Player ♦ Complex Offline Method ♦ Player Action ♦ Recent Year ♦ Viable Model ♦ Related Research ♦ Next-generation Digital Game ♦ Novel Enhancement ♦ Player Modelling
Description 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 roleplaying 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 classifier to a level near those of more advanced and complex offline methods.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Institution In Proceedings of the second Artificial Intelligence and Interactive Digital Entertainment conference (AIIDE-06