Knowledge-Directed Theory Revision
Using domain knowledge to speed up learning is widely accepted but theory revision of such knowledge continues to use general syntactic operators. Using such operators for theory revision of teleoreactive logic programs is especially expensive in which proof of a top-level goal involves playing a game. In such contexts, one should have the option to complement general theory revision with domain-specific knowledge. Using American football as an example, we use Icarus’ multi-agent teleoreactive logic programming ability to encode a coach agent whose concepts correspond to faults recognized in execution of the play and whose skills correspond to making repairs in the goals of the player agents. Our results show effective learning using as few as twenty examples. We also show that structural changes made by such revision can produce performance gains that cannot be matched by doing only numeric optimization.
KeywordsLogic Program Belief Revision American Football Theory Revision Revision Rule
Unable to display preview. Download preview PDF.
- 1.Langley, P., Choi, D.: A unified cognitive architecture for physical agents. In: Proceedings of the Twenty-First National Conference on Artificial Intelligence, Boston. AAAI Press, Menlo Park (2006)Google Scholar
- 2.Wilkins, D., Myers, K., Lowrance, J., Wesley, L.: A multiagent planning architecture. In: Proceedings of AIPS 1998 (1998)Google Scholar
- 3.Gardenfors, P.: Belief revision and nonomotonic logic: Two sides of the same coin? In: Proceedings of the ninth European Conference on Artificial Intelligence. Pitman Publishing (1990)Google Scholar
- 4.Sourceforge: Sourceforge.net - rush 2005 (2005), http://sourceforge.net/projects/rush2005/
- 5.Hess, R., Fern, A.: Discriminatively trained particle filters for complex multi-object tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (2009)Google Scholar