@article{PortinaleTorasso01,
title = {Case-Base Maintenance in a Multimodal Reasoning System},
author = {Luigi Portinale and Pietro Torasso},
journal = {Computational Intelligence},
month = {May},
number = {2},
pages = {263--279},
volume = {17},
year = {2001},
abstract = {The definition of suitable case-base maintenance policies is
widely recognized as a major key to success for case-based
systems; underestimating this issue may lead to systems that
either do not fulfill their role of knowledge management and
preservation or that do not perform adequately under performance
dimensions, namely, computation time and competence and quality
of solutions. The goal of this article is to analyze some
automatic case-base management strategies in the context of a
multimodal architecture combining case-based reasoning and
model-based reasoning. We propose and compare two different
methodologies, the first one, called replace, is a
competence-based strategy aimed at replacing a set of stored
cases with the current one, if the latter exhibits an estimated
competence comparable with the estimated competence of the
considered set of stored cases. The second one, called learning
by failure with forgetting (LFF), is based on incremental
learning of cases interleaved with off-line processes of
forgetting (deleting) cases whose usage does not fulfill specific
utility conditions. Results from an extensive experimental
analysis in an industrial plant diagnosis domain are reported,
showing the usefulness of both strategies with respect to the
maintenance of suitable performance levels for the target system.},
keywords = {imported }
}
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