Quantitative results concerning the utility of explanation-based learning

摘要

In order to solve problems effectively, a problem solver must be able to exploit domain-specific search control knowledge. Although previous research has demonstrated that explanation-based learning is a viable approach for acquiring such knowledge, in practice the control knowledge learned via EBL may not be useful. To be useful, the cumulative benefits of applying the knowledge must outweigh the cumulative costs of testing whether the knowledge is applicable. Unlike most previous systems that use EBL, the PRODIGY system evaluates the costs and benefits of the control knowledge it learns. Furthermore, the system produces useful control knowledge by actively searching for “good” explanations—explanations that can be profitably employed to control problem solving. This paper summarizes a set of experiments measuring the effectiveness of PRODIGY's EBL method (and its components) in several different domains.