Learning Planning Operators by Observation and Practice

Abstract

The woik described in this paper addresses leaming planning operators by observing expert agents and subsequent knowledge refinement in a leaming-by-doing paradigm. The observations of the expert agent consist of: 1) the sequence of actions being executed, 2) the state in which each action is executed, and 3) the state resulting from the execution of each action. Planning operators are learned from these observation sequences in an incremental fashion utilizing a conservative specific-to-general inductive generalization process. In order to refine the new operators to make them correct and complete, the system uses the new operators to solve practice problems, analyzing and leaming from the execution traces of the resulting solutions or execution failures. We describe techniques for planning and plan repair with incorrect and incomplete domain knowledge, and for operator refinement through a process which integrates planning, execution, and plan repair. Our learning method is implemented on top of the PRODIGY architecture(Carbonell, Knoblock, & Minton 1990; Carbonell et al. 1992) and is demonstrated in the extended-strips domain(Minton 1988) and a subset of the process planning domain(Gil 1991).