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Automatic case base management in a multi-modal reasoning system

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationAdvances in Case-Based Reasoning - 5th European Workshop, EWCBR 2000, Proceedings
EditorsEnrico Blanzieri, Luigi Portinale
PublisherSpringer Verlag
Pages234-246
Number of pages13
ISBN (Print)3540679332, 9783540679332
DOIs
Publication statusPublished - 2000
Event5th European Workshop on Case-Based Reasoning, EWCBR 2000 - Trento, Italy
Duration: 6 Sept 20009 Sept 2000

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1898
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th European Workshop on Case-Based Reasoning, EWCBR 2000
Country/TerritoryItaly
CityTrento
Period6/09/009/09/00

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