Acquiring recursive and iterative concepts with explanation-based learning

摘要

Inexplanation-based learning, a specific problem's solution is generalized into a form that can be later used to solve conceptually similar problems. Most research in explanation-based learning involves relaxing constraints on the variables in the explanation of a specific example, rather than generalizing thegraphical structure of the explanation itself. However, this precludes the acquisition of concepts where an iterative or recursive process is implicitly represented in the explanation by a fixed number of applications. This paper presents an algorithm that generalizes explanation structures and reports empirical results that demonstrate the value of acquiring recursive and iterative concepts. Thebagger2 algorithm learns recursive and iterative concepts, integrates results from multiple examples, and extracts useful subeoncepts during generalization. On problems where learning a recursive rule is not appropriate, the system produces the same results as standard explanation-based methods. Applying the learned recursive rules only requires a minor extension to aprolog-like problem solver, namely, the ability to explicitly call a specific rule. Empirical studies demonstrate that generalizing the structure of explanations helps avoid the recently reported negative effects of learning.