Learning Planning Operators by Observation and Practice.

Abstract

Acquiring and maintaining domain knowledge is a key bottleneck in applications of planning systems. This thesis describes a machine learning approach to automatic acquisition of planning operators. Our approach is to learn planning operators by observing expert solution traces and to refine operators through practice in a learning-by-doing paradigm. During observation, our system uses the knowledge that is observable when experts solve problems, without the need of explicit instruction or interrogation. During practice, our system generates its own learning opportunities by solving practice problems. 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. The output is a set of operators, each described by a list of variables, preconditions, and effects. The operators are learned incrementally using an inductive algorithm. During practice, our system effective...