Craig Boutilier and Veronica Becher
Department of Computer Science
University of British Columbia
Vancouver, BC, CANADA, V6T 1Z4
email: cebly@cs.ubc.ca
Abstract
We propose a model of abduction based on the revision
of the epistemic state of an agent. Explanations
must be sufficient to induce belief in the sentence to be
explained (for instance, some observation), or ensure its
consistency with other beliefs, in a manner
that adequately accounts for factual and hypothetical sentences.
Our model will generate explanations that nonmonotonically predict
an observation, thus generalizing most current accounts, which
require some deductive relationship between explanation and
observation. It also provides a natural preference ordering on
explanations, defined in terms of normality or
plausibility. To illustrate the generality of
our approach, we reconstruct two of the key paradigms for
model-based diagnosis, abductive and consistency-based
diagnosis, within our framework. This reconstruction provides
an alternative semantics for both and
extends these systems to
accommodate our predictive explanations and
semantic preferences on explanations. It also illustrates
how more general information can be incorporated in a principled manner.
(To appear, Artificial Intelligence, 1995)
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