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Proceedings Paper

General-purpose abductive algorithm for interpretation
Author(s): Richard K. Fox; Julie Hartigan
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Paper Abstract

Abduction, inference to the best explanation, is an information-processing task that is useful for solving interpretation problems such as diagnosis, medical test analysis, legal reasoning, theory evaluation, and perception. The task is a generative one in which an explanation comprising of domain hypotheses is assembled and used to account for given findings. The explanation is taken to be an interpretation as to why the findings have arisen within the given situation. Research in abduction has led to the development of a general-purpose computational strategy which has been demonstrated on all of the above types of problems. This abduction strategy can be performed in layers so that different types of knowledge can come together in deriving an explanation at different levels of description. Further, the abduction strategy is tractable and offers a very useful tradeoff between confidence in the explanation and completeness of the explanation. This paper will describe this computational strategy for abduction and demonstrate its usefulness towards perceptual problems by examining problem-solving systems in speech recognition and natural language understanding.

Paper Details

Date Published: 11 November 1996
PDF: 12 pages
Proc. SPIE 2824, Adaptive Computing: Mathematical and Physical Methods for Complex Environments, (11 November 1996); doi: 10.1117/12.258124
Show Author Affiliations
Richard K. Fox, Univ. of Texas/Pan American (United States)
Julie Hartigan, TASC, Inc. (United States)


Published in SPIE Proceedings Vol. 2824:
Adaptive Computing: Mathematical and Physical Methods for Complex Environments
H. John Caulfield; Su-Shing Chen, Editor(s)

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