Automatic Case Base Management in a
Multi-modal Reasoning System

Luigi Portinale1 and Pietro Torasso2

1 Dipartimento di Scienze e Tecnologie Avanzate, DISTA
Universita del Piemonte Orientale A. Avogadro
C.so Borsalino 54 - 15100 Alessandria (ITALY)
portinal@unipmn.it
2 Dipartimento di Informatica - Universita di Torino
C.so Svizzera 185 - 10149 Torino (ITALY)
torasso@di.unito.it




Abstract. The definition of suitable case base maintenance policies is
widely recognized as a major success key of CBR systems; underestimating 
this issue may lead to systems that that do not perform adequately
under performance dimensions, namely computation time, competence
and quality of solutions. The goal of the present paper is to analyse an
automatic case base management strategy in the context of multi-modal
architectures combining CBR and Model-Based Reasoning. The strategy,
called Learning by Failure with Forgetting (LFF ) is based on incremental 
learning of cases interleaved with off-line processes of case deletion,
in order to control the content and the size of the case library. Results
from an extensive experimental analysis in an industrial plant diagnosis
domain is then reported, showing the usefulness of LFF with respect
to the maintenance of suitable performance level for the target system.
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