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Master's Dissertation
DOI
10.11606/D.55.2018.tde-20042018-090008
Document
Author
Full name
Alneu de Andrade Lopes
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 1995
Supervisor
Committee
Monard, Maria Carolina (President)
Camargo, Heloisa de Arruda
Rillo, Marcio
Title in Portuguese
RACIOCÍNIO BASEADO EM CASOS
Keywords in Portuguese
Não disponível
Abstract in Portuguese
Raciocínio Baseado em Casos (RBC) é um paradigma de Inteligência Artificial (IA) que, em essência, busca utilizar uma experiência prévia para entender e resolver um problema novo. O objetivo central deste trabalho é apresentar em detalhes esse paradigma de IA, analisar seus principais temas e propor uma metodologia para o desenvolvimento de um sistema de RBC. Além disso, a metodologia proposta é avaliada através da implementação dos principais algoritmos relacionados à: representação de conhecimento utilizando casos, indexação e armazenamento de casos, métricas de similaridade para recuperação e adaptação de casos.
Title in English
Not available
Keywords in English
Not available
Abstract in English
Artificial Intelligence engineers seek to automate mental processes to solve new problems. In Case-based Reasoning (CBR) this is done based on the following idea: a case-based reasoner solves new problems by adapting solutions that were used to solve old problems. CBR systems are characterized by a database of structured informafion, called cases, indexing for rapid access to selected portions of the database as well as methods for determining the similarity of stored cases and users' supply cases. In this work we discuss in some details those characteristics and propose a new methodology for developing CBR systems. Memory organization as well as the main algorithms for indexing and similarity metrics to retrieve and adapt cases, related to the proposed methodology are also presented.
 
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AlneudeAndradeLopes.pdf (107.73 Mbytes)
Publishing Date
2018-04-20
 
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