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
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
Poniters to these and other related papers can be found on our Abduction research page.
- 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.
- 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.