Date of Original Version
Abstract or Description
Acquiring knowledge from experts for planning systems is a rather difficult knowledge engineering task, but is essential for any applications of planning systems. This work addresses the issue of automatic acquisition of planning operators. Operators are learned by observing the solution traces of experts agents and by subsequently refining knowledge in a learning-by-doing paradigm. It is domain-independent and assumes minimal requirements for a priori knowledge and expert involvement in order to reduce the burden on the knowledge engineerer and domain experts. 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 learning 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).