TitleConstucting game agents from video of human behavior
Publication TypeConference Paper
Year of Publication2009
AuthorsLi, N, Stracuzzi, DJ, Cleveland, G, Konik, T, Shapiro, D, Molineaux, M, Aha, DW, Ali, K
Conference NameConference on Artificial Intelligence and Interactive Digital Entertainment
PublisherAAAI Press
Conference LocationStanford, CA
Keywordscognitive architectures, game AI, machine learning, skill learning

Developing computer game agents is often a lengthy and expensive undertaking. Detailed domain knowledge and decision-making procedures must be encoded into the agent to achieve realistic behavior. In this paper, we simplify this process by using the ICARUS cognitive architecture to construct game agents. The system acquires structured, high fidelity methods for agents that utilize a vocabulary of concepts familiar to game experts. We demonstrate our approach by first acquiring behaviors for football agents from video footage of college football games, and then applying the agents in a football simulator.

Refereed DesignationRefereed
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machine learning
game AI
cognitive architectures
skill learning