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.
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Mozgovoy, M., Umarov, I. (2011). Behavior Capture with Acting Graph: A Knowledgebase for a Game AI System. In: Kikuchi, S., Madaan, A., Sachdeva, S., Bhalla, S. (eds) Databases in Networked Information Systems. DNIS 2011. Lecture Notes in Computer Science, vol 7108. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25731-5_7
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DOI: https://doi.org/10.1007/978-3-642-25731-5_7
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