p-95-07

Abduction, Experience, and Goals:  A Model of Everyday Abductive Explanation 

David B. Leake

The Journal of Experimental and Theoretical Artificial Intelligence.
7:407-428, 1995.

Abstract

Many abductive understanding systems generate explanations by a
backwards chaining process that is neutral both to the explainer's
previous experience in similar situations and to why the explainer is
attempting to explain.  This article examines the relationship of such
models to an approach that uses case-based reasoning to generate
explanations.  In this case-based model, the generation of abductive
explanations is focused by prior experience and by goal-based criteria
reflecting current information needs.  The article analyzes the
commitments and contributions of this case-based model as applied to
the task of building good explanations of anomalous events in everyday
understanding.  The article identifies six central issues for
abductive explanation, compares how these issues are addressed in
traditional and case-based explanation models, and discusses benefits
of the case-based approach for facilitating generation of plausible
and useful explanations in domains that are complex and imperfectly
understood.


A postscript file for the full paper is available electronically.  
To get a copy
  by anonymous ftp, see  ftp://ftp.cs.indiana.edu/pub/leake/README.
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