Article,
Competence Models and the Maintenance Problem
Computational Intelligence, 17 (2): 235--249 (May 2001)
Abstract
Case-based reasoning (CBR) systems solve problems by retrieving
and adapting the solutions to similar problems that have been
stored previously as a case base of individual problem solving
episodes or cases. The maintenance problem refers to the problem
of how to optimize the performance of a CBR system during its
operational lifetime. It can have a significant impact on all the
knowledge sources associated with a system (the case base, the
similarity knowledge, the adaptation knowledge, etc.), and over
time, any one, or more, of these knowledge sources may need to be
adapted to better fit the current problem-solving environment.
For example, many maintenance solutions focus on the maintenance
of case knowledge by adding, deleting, or editing cases. This has
lead to a renewed interest in the issue of case competence, since
many maintenance solutions must ensure that system competence is
not adversely affected by the maintenance process. In fact, we
argue that ultimately any generic maintenance solution must
explicitly incorporate competence factors into its maintenance
policies. For this reason, in our work we have focused on
developing explanatory and predictive models of case competence
that can provide a sound foundation for future maintenance
solutions. In this article we provide a comprehensive survey of
this research, and we show how these models have been used to
develop a number of innovative and successful maintenance
solutions to a variety of different maintenance problems.
