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<title>The ACCEL Abductive Reasoning System</title><h1>ACCEL</h1><br>ACCEL is a general purpose system that uses abductive reasoning toconstruct explanations for observed intelligent phenomena. Theseexplanations are then used to avoid redundant work in future problemsolving episodes. We define an abductive explanation as a consistentset of assumptions which when combined with background knowledge,logically entails a set of observations. <p>ACCEL has been constructed as a domain-independent system, in whichknowledge about a variety of domains has been uniformly encoded asfirst-order Horn-clause axioms. A general-purpose abductionalgorithm, AAA, is used to efficiently construct explanations bycaching partial explanations. ACCEL has been shown to achieve morethan an order of magnitude speedup in run time for a variety ofdomains, including plan recognition in text understanding, anddiagnosis of medical diseases, logic circuits, and dynamicsystems. <p>Common Lisp source code for the ACCEL system and several diagnosisdomains is available via <!WA0><!WA0><a href="ftp://ftp.cs.utexas.edu/pub/mooney/accel"> anonymous FTP </a>. <p>A more detailed description of this system can be found in thefollowing publications: <p><ol><li> Hwee Tou Ng and Raymond Mooney, "Abductive Plan Recognition and Diagnosis: A Comprehensive Empirical Evaluation," <cite>Proceedings of the Third International Conference on Principles of Knowledge Representation and Reasoning</cite> (KR-92), pp. 499-508, Cambridge, MA, October 1992. <li> Hwee Tou Ng and Raymond Mooney, "An Efficient First-Order Horn-Clause Abduction System Based on the ATMS," <cite>Proceedings of the Ninth National Conference on Artificial Intelligence</cite>, pages 494-499, Anaheim, CA, July 1991. </ol>Poniters to these and other related papers can be found on our <!WA1><!WA1><ahref="http://www.cs.utexas.edu/users/ml/abduction.html">Abduction</a> research page.<p><hr><address><!WA2><!WA2><a href="http://www.cs.utexas.edu/users/estlin/">estlin@cs.utexas.edu</a></address>
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