Donald Bren School of Information and Computer Sciences

This paper presents a knowledge-based learning method and reports on case studies in different domains. The method integrates abduction and learning. Abduction provides an improved method for constructing explanations, in comparison with deductive methods traditionally associated with explanation-based learning. The improvement enlarges the set of examples that can be explained so that one can learn from additional examples using explanation-based macro-learning techniques. Abduction also provides a form of knowledge level learning. The importance of abductive learning is shown by case studies involving 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 pratical tasks including: natural langauge understanding and plan recognition; qualitative physical reasoning and postdiction; diagnosis and signal interpretation; and decision-making under uncertainty. The descriptions of the case studies show how to set up abduction engines in particular domains and how abduction solves particular tasks. They show how to provide and how to represent the relevant knowledge, including both domain knowledge and meta-knowledge relevant to abduction and search control. The description of each case study includes an example, its explanation, and discussions of what is learned by macro-learning and by abductive inference.