ABDUCTION and EXPLANATION‐BASED LEARNING: CASE STUDIES IN DIVERSE DOMAINS

Abstract

This paper presents a knowledge-based learning method and reports on case studies in different domains. The method integrates abduction and explanation-based learning. Abduction provides an improved method for constructing explanation$. The improvement enlarges the set of examples that can be explained so that one can learn from additional examples using traditional explanation-based macro learning. Abduction also provides a form of knowledge level learning. Descriptions of case studies show how to set up abduction engines for tasks in particular domains. The case studies involve over a hundred examples taken from diverse domains requiring logical, physical, and psychological knowledge and reasoning. The case studies are relevant to a wide range of practical tasks including natural language understanding and plan recognition; qualitative physical reasoning and postdiction; diagnosis and signal interpretation; and decision making under uncertainty. The descriptions of the case studies include an example, its explanation, and discussions of what is learned by macro-learning and by abductive inference. The paper discusses how to provide and represent the domain knowledge and meta-knowledge needed for abduction and search control. The main conclusion is that abductive inference is important for learning. Abduction and macro-learning are complementary and synergistic.

Integrating induction and abduction in logic programming

“…The relationship between abduction and induction has been studied recently by several authors, mainly in the field of Artificial Intelligence (see, for instance, Refs. [8,34]). In general, the question of how abduction and induction could be integrated and how they would cooperate, complement and affect each other is emerging as an important problem and has been the subject of two international workshops (see Refs.…”