Learning by Observation and Practice: An Incremental Approach for Planning Operator Acquisition

TLDR: Empirical results show that the system learns operators in this domain well enough to solve problems as effectively as human-expert coded operators.

Abstract: This paper describes an approach to automatically learn planning operators by observing expert solution traces and to further refine the operators through practice in a learning-by-doing paradigm. This approach uses the knowledge naturally observable when experts solve problems, without need of explicit instruction or interrogation. The inputs to our learning system are: the description language for the domain, experts' problem solving traces, and practice problems to allow learning-by-doing operator refinement. Given these inputs, our system automatically acquires the preconditions and effects (including conditional effects and preconditions) of the operators. We present empirical results to demonstrate the validity of our approach in the process planning domain. These results show that the system learns operators in this domain well enough to solve problems as effectively as human-expert coded operators. Our approach differs from knowledge acquisition tools in that it does not require a considerable amount of direct interactions with domain experts. It differs from other work on automatically learning operators in that it does not require initial approximate planning operators or strong background knowledge.