Constructing Game Agents from Video of Human Behavior

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.

[1]  David J. Stracuzzi,et al.  Representing and Reasoning over Time in a Symbolic Cognitive Architecture , 2009 .

[2]  Hector Muñoz-Avila,et al.  HTN-MAKER: Learning HTNs with Minimal Additional Knowledge Engineering Required , 2008, AAAI.

[3]  Jeff Orkin,et al.  Three States and a Plan: The A.I. of F.E.A.R. , 2006 .

[4]  Verzekeren Naar Sparen,et al.  Cambridge , 1969, Humphrey Burton: In My Own Time.

[5]  Gillian Hayes,et al.  Group utility functions: learning equilibria between groups of agents in computer games by modifying the reinforcement signal , 2005, 2005 IEEE Congress on Evolutionary Computation.

[6]  Pat Langley,et al.  Learning Hierarchical Skills from Problem Solutions Using Means-Ends Analysis , 2009 .

[7]  Richard Reviewer-Granger.  Unified Theories of Cognition , 1991, Journal of Cognitive Neuroscience.

[8]  Pat Langley,et al.  Representing and Reasoning over Time in a Unified Cognitive Architecture , 2009 .

[9]  Pat Langley,et al.  Learning hierarchical task networks by observation , 2006, ICML.

[10]  John E. Laird,et al.  EXAMPLE-DRIVEN DIAGRAMMATIC TOOLS FOR RAPID KNOWLEDGE ACQUISITION , 2003 .

[11]  Alan Fern,et al.  Mixture-of-Parts Pictorial Structures for Objects with Variable Part Sets , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[12]  William W. Cohen,et al.  Proceedings of the 23rd international conference on Machine learning , 2006, ICML 2008.

[13]  Kenneth D. Forbus,et al.  Companion Cognitive Systems: A Step towards Human-Level AI , 2004, AI Mag..

[14]  Adi Botea,et al.  Offline Planning with Hierarchical Task Networks in Video Games , 2007, AIIDE.

[15]  Michael Mateas,et al.  Proceedings of the Fourth Artificial Intelligence and Interactive Digital Entertainment Conference, October 22-24, 2008, Stanford, California, USA , 2008, AIIDE.

[16]  John E. Laird,et al.  Soar-RL: integrating reinforcement learning with Soar , 2005, Cognitive Systems Research.

[17]  Pat Langley,et al.  A Unified Cognitive Architecture for Physical Agents , 2006, AAAI.

[18]  Alan Fern,et al.  Discriminatively trained particle filters for complex multi-object tracking , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Allen Newell,et al.  Chunking in Soar: The anatomy of a general learning mechanism , 1985, Machine Learning.

[20]  John R. Anderson.  A spreading activation theory of memory. , 1983 .