Inductive generalization of analytically learned goal hierarchies

  • Authors:

  • Tolga Könik;Negin Nejati;Ugur Kuter

  • Affiliations:

  • Computational Learning Laboratory, Stanford University, Stanford, CA;Computational Learning Laboratory, Stanford University, Stanford, CA;Department of Computer Science and Institute for Advanced Computer Studies, University of Maryland, College Park, MD

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

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Abstract

We describe a new approach for learning procedural knowledge represented as teleoreactive logic programs using relational behavior traces as input. This representation organizes task decomposition skills hierarchically and associate explicitly defined goals with them. Our approach integrates analytical learning with inductive generalization in order to learn these skills. The analytical component predicts the goal dependencies in a successful solution and generates a teleoreactive logic program that can solve similar problems by determining the structure of the skill hierarchy and skill applicability conditions (preconditions), which may be overgeneral. The inductive component experiments with these skills on new problems and uses the data collected in this process to refine the preconditions. Our system achieves this by converting the data collected during the problem solving experiments into the positive and negative examples of preconditions that can be learned with a standard Inductive Logic Programming system. We show that this conversion uses one of the main commitments of teleoreactive logic programs: associating all skills with explicitly defined goals. We claim that our approach uses less expert effort compared to a purely inductive approach and performs better compared to a purely analytical approach.