Learning Planning Operators by Exploration and Experimentation

Contents

This paper addresses a computational approach to the automated acquisition of domain knowledge for planning systems via experimentation with the environment. Previous work showed how existing incomplete operators can be refreed by adding missing preconditions and effects. Here we develop additional methods to acquire new operators such as direct analogy with existing operators, decomposition of monolithic operators into meaningful sub-operators, and experimentation with partially-specified operators.