Knowledge-directed theory revision

  • Authors:

  • Kamal Ali;Kevin Leung;Tolga Konik;Dongkyu Choi;Dan Shapiro

  • Affiliations:

  • Cognitive Systems Laboratory, Center for the Study of Language and Information, Stanford University, Stanford, CA;Cognitive Systems Laboratory, Center for the Study of Language and Information, Stanford University, Stanford, CA;Cognitive Systems Laboratory, Center for the Study of Language and Information, Stanford University, Stanford, CA;Cognitive Systems Laboratory, Center for the Study of Language and Information, Stanford University, Stanford, CA;Cognitive Systems Laboratory, Center for the Study of Language and Information, Stanford University, Stanford, CA

  • Venue:
  • ILP'09 Proceedings of the 19th international conference on Inductive logic programming
  • Year:
  • 2009

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Abstract

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.