Lazy Incremental Learning of Control Knowledge for Eeciently Obtaining Quality Plans

General-purpose generative planners use domain-independent search heuristics to generate solutions for problems in a variety of domains. However, in some situations these heuristics force the planner to perform ineeciently or obtain solutions of poor quality. Learning from experience can help to identify the particular situations for which the domain-independent heuristics need to be overridden. Most of the past learning approaches are fully deductive and eagerly acquire correct control knowledge from a necessarily complete domain theory and a few examples to focus their scope. These learning strategies are hard to generalize in the case of nonlinear planning, where it is diicult to capture correct explanations of the interactions among goals, multiple planning operator choices, and situational data. In this article, we present a lazy learning method that combines a deductive and an inductive strategy to eeciently learn control knowledge incrementally with experience. We present hamlet, a system we developed that learns control knowledge to improve both search eeciency and the quality of the solutions generated by a nonlinear planner, namely prodigy4.0. We have identiied three lazy aspects of our approach from which we believe hamlet greatly beneets: lazy explanation of successes, incremental reenement of acquired knowledge, and lazy learning to override only the default behavior of the problem solver. We show empirical results that support the eeectiveness of this overall lazy learning approach, in terms of improving the eeciency of the problem solver, and the quality of the solutions produced.