Constructing and refining causal explanations from an inconsistent domain theory
Richard J. Doyle · 1986
Recent work in the field of machine learning has demon-strated the utility of explanation formation as a guide to gen-eralization. Most of these investigations have concentrated on the formation of explanations from consistent domain theories. I present an approach to forming explanations from domain the-ories which are inconsistent due to the presence of abstractions which suppress potentially relevant detail. In this approach, ex-planations are constructed to support reasoning tasks and are refined in a failure-driven manner. The elaboration of explana-tions is guided by the structuring of domain theories into layers of abstractions. This work is part of a larger effort to develop a causal mod-elling system which forms explanations of the underlying causal relations in physical systems. This system utilizes an inconsis-