Use this identifier to quote or link this document: http://hdl.handle.net/2072/13262

Reactive and proactive approaches for introspective CBR
Mülâyim, Mehmet Oguz
Universitat Autònoma de Barcelona. Departament de Ciències de la Computació; Arcos Rossell, Josep Lluís
This work investigates applying introspective reasoning to improve the performance of Case-Based Reasoning (CBR) systems, in both reactive and proactive fashion, by guiding learning to improve how a CBR system applies its cases and by identifying possible future system deficiencies. First we present our reactive approach, a new introspective reasoning model which enables CBR systems to autonomously learn to improve multiple facets of their reasoning processes in response to poor quality solutions. We illustrate our model’s benefits with experimental results from tests in an industrial design application. Then as for our proactive approach, we introduce a novel method for identifying regions in a case-base where the system gives low confidence solutions to possible future problems. Experimentation is provided for Zoology and Robo-Soccer domains and we argue how encountered regions of dubiosity help us to analyze the case-bases of a given CBR system.
2008-10
68 - Indústries, oficis i comerç d'articles acabats. Tecnologia cibernètica i automàtica
Raonament basat en casos
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