AAAI Publications, Twenty-Third International FLAIRS Conference

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Improving Structural Knowledge Transfer with Parametric Adaptation
Tolga Konik, Kamal Ali, Daniel Shapiro, Nan Li, David J. Stracuzzi

Last modified: 2010-05-06


Transfer of learned knowledge from one task to another offers an opportunity to reduce development cost of knowledge-based systems by reusing existing knowledge in novel situations. However, minor differences in the initial and target environments can reduce the effectiveness of the system substantially. In previous work, we presented a system that acquired procedural knowledge of American football from video footage, and then applied it to controlling players in a simulated environment. In this paper, we extend that system by adding the ability to adapt the transferred procedures to better fit the simulator. We show that even when the transferred structural knowledge provides a quality starting point for performance in the game environment, a simple parameter optimization technique can significantly improve its performance and utility.


computer games;game AI;structural transfer learning;learning by observation;cognitive agents;behavioral cloning

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