ACCEL


ACCEL is a general purpose system that uses abductive reasoning to construct explanations for observed intelligent phenomena. These explanations are then used to avoid redundant work in future problem solving episodes. We define an abductive explanation as a consistent set of assumptions which when combined with background knowledge, logically entails a set of observations.

ACCEL has been constructed as a domain-independent system, in which knowledge about a variety of domains has been uniformly encoded as first-order Horn-clause axioms. A general-purpose abduction algorithm, AAA, is used to efficiently construct explanations by caching partial explanations. ACCEL has been shown to achieve more than an order of magnitude speedup in run time for a variety of domains, including plan recognition in text understanding, and diagnosis of medical diseases, logic circuits, and dynamic systems.

Common Lisp source code for the ACCEL system and several diagnosis domains is available via anonymous FTP .

A more detailed description of this system can be found in the following publications:

  1. Hwee Tou Ng and Raymond Mooney, "Abductive Plan Recognition and Diagnosis: A Comprehensive Empirical Evaluation," Proceedings of the Third International Conference on Principles of Knowledge Representation and Reasoning (KR-92), pp. 499-508, Cambridge, MA, October 1992.
  2. Hwee Tou Ng and Raymond Mooney, "An Efficient First-Order Horn-Clause Abduction System Based on the ATMS," Proceedings of the Ninth National Conference on Artificial Intelligence, pages 494-499, Anaheim, CA, July 1991.
Poniters to these and other related papers can be found on our Abduction research page.

estlin@cs.utexas.edu