A Multistrategy Learning System for Planning Operator Acquisition

Third International Conference on Multistrategy Learning

Volume

Third International Conference on Multistrategy Learning

Contents

This paper describes a multistrategy learning approach for automatic acquisition of planning operators. The two strategies are: (i) learning operators by observing expert solution traces, and (ii) refining operators through practice in a learning-by-doing paradigm. During observation, OBSERVER uses the knowledge that is naturally observable when experts solve problems, without the need of explicit instruction or interrogation. During practice, OBSERVER 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. Given these inputs, our system automatically acquires the preconditions and effects (including conditional effects and preconditions) of the operators.