Abstract
Behavior capture is a popular experimental approach used to obtain human-like AI-controlled game characters through learning by observation and case-based reasoning. One of the challenges related to the development of behavior capture-based AI is the choice of appropriate data structure for agents' memory. In this paper, we consider the advantages of acting graph as a memory model and discuss related techniques, successfully applied in several experimental projects, dedicated to the creation of human-like behavior. © 2011 Springer-Verlag Berlin Heidelberg.
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Mozgovoy, M., & Umarov, I. (2011). Behavior capture with acting graph: A knowledgebase for a game AI system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7108 LNCS, pp. 68–77). https://doi.org/10.1007/978-3-642-25731-5_7
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