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COGNITIVE AND BEHAVIORAL MODEL ENSEMBLES FOR AUTONOMOUS VIRTUAL CHARACTERS
Author(s) -
Whiting Jeffrey S.,
Dinerstein Jonathan,
Egbert Parris K.,
Ventura Dan
Publication year - 2010
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.2009.00345.x
Subject(s) - computer science , programmer , behavioral modeling , artificial intelligence , set (abstract data type) , cognitive model , leverage (statistics) , character (mathematics) , cognition , process (computing) , human–computer interaction , animation , machine learning , aggregate (composite) , programming language , psychology , materials science , geometry , mathematics , computer graphics (images) , neuroscience , composite material
Cognitive and behavioral models have become popular methods for creating autonomous self‐animating characters. Creating these models present the following challenges: (1) creating a cognitive or behavioral model is a time‐intensive and complex process that must be done by an expert programmer and (2) the models are created to solve a specific problem in a given environment and because of their specific nature cannot be easily reused. Combining existing models together would allow an animator, without the need for a programmer, to create new characters in less time and to leverage each model's strengths, resulting in an increase in the character's performance and in the creation of new behaviors and animations. This article provides a framework that can aggregate existing behavioral and cognitive models into an ensemble. An animator has only to rate how appropriately a character performs in a set of scenarios and the system then uses machine learning to determine how the character should act given the current situation. Empirical results from multiple case studies validate the approach.