Using Distribution-Free Learning Theory to Analyze Solution Path Caching Mechanisms

Much research in machine learning has been focused on the problem of symbol- level learning (SLL), or learning improve the performance of a given examples of its behavior on typical inputs. A common approach to symbol level learning is to use some sort of mechanisms are macro-operator learning, explanation-based learning, and chucking.