Learning by Experimentation: The Operator Refinement Method
CARNEGIE-MELLON UNIV PITTSBURGH PA ARTIFICIAL INTELLIGENCE AND PSYCHOLOGY PROJECT
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Autonomous systems require the ability to plan effective courses of action under potentially uncertain or unpredictable contingencies. Effective planning requires knowledge of the environment, and if the environment is too complex or changes dynamically, goal-driven learning with reactive feedback becomes a necessity. This paper addresses the issue of learning by experimentation as an integral component of PRODIGY, a flexible planning system augmented with capabilities for execution monitoring and dynamic replanning upon receiving adverse feedback. PRODIGY encodes its domain knowledge as declarative operators, and applies the operator refinement method to acquire additional preconditions or postconditions for its operators when observed consequences diverge from internal expectations. When multiple explanations for the observed divergence are consistent with the existing domain knowledge, experiments to discriminate among these explanations are generated. Thus, experimentation is demand-driven and exploits both the internal state of the planner and any external feedback received. A detailed example of integrated experiment formulation in presented as the basis for a systematic approach to extending an incomplete domain theory or correcting a potentially inaccurate one. Keywords Machine learning, Planning, Experimentation, Problem solving.