One-Sided Algorithms for Integrating Empirical and Explanation-Based Learning

Abstract

This chapter discusses a framework for integrating empirical learning with explanation-based learning and presents an algorithm that does this with both pure conjunctive concepts and k-CNF concepts. The framework involves using an empirical and an explanation-based method to form separate hypotheses, and then combining the hypotheses from the separate sources to form a composite hypothesis. An additional important complication arises because the system is required to learn the domain theory (via an empirical method) while using the domain theory to support the explanation-based method. The hypotheses produced by explanation-based learning with a domain theory acquired with such a one-sided empirical learning method will also never be more general than the correct hypothesis. As both the empirical and explanation-based hypotheses are not more general than the correct hypothesis, they can be combined by finding the least general hypothesis consistent with both hypotheses. In this manner, the integrated hypothesis would be the least general hypothesis that is consistent with both the observed data and the domain knowledge.