A diverse set of intelligent activities, including natural language understanding and diagnosis, requires the ability to construct explanations for observed phenomena. In this paper, we view explanation as abduction, where an abductive explanation is a consistent set of assumptions which, together with background knowledge, logically entails a set of observations. We have successfully built a domain-independent system, ACCEL, in which knowledge about a variety of domains is uniformly encoded in first-order Horn-clause axioms. A general-purpose abduction algorithm, AAA, efficiently constructs explanations in the various domains by caching partial explanations to avoid redundant work. Empirical results show that caching of partial explanations can achieve more than an order of magnitude speedup in run time. We have applied our abductive system to two general tasks: plan recognition in text understanding, and diagnosis of medical diseases, logic circuits, and dynamic systems. The results indicate that ACCEL is a general-purpose system capable of plan recognition and diagnosis, yet efficient enough to be of practical utility.

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unpublished. Unpublished Technical Note.

@unpublished{accel-note-94, title={A First-Order Horn-Clause Abductive System and Its Use in Plan Recognition and Diagnosis}, author={Hwee Tou Ng and Raymond J. Mooney}, month={June}, note={Unpublished Technical Note}, url="http://www.cs.utexas.edu/users/ai-lab?accel-note-94", year={1992} }