Search and Adaptation in a Fuzzy Object Oriented Case Base

Ruet, Magali; Geneste, Laurent

doi:10.1007/3-540-46119-1_26

Cited by 11 publications

(10 citation statements)

References 8 publications

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“…Much literature based on such a fuzzy-CBR approach has been published. Examples include Hirota et al (1997), de Mantaras andPlaza (1997), Ruet and Geneste (2002), and Chan (2005).…”

Section: Case-based Reasoningmentioning

confidence: 97%

A fuzzy CBR technique for generating product ideas

Hsu

2008

Expert Systems with Applications

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“…Much literature based on such a fuzzy-CBR approach has been published. Examples include Hirota et al (1997), de Mantaras andPlaza (1997), Ruet and Geneste (2002), and Chan (2005).…”

Section: Case-based Reasoningmentioning

confidence: 97%

A fuzzy CBR technique for generating product ideas

Hsu

2008

Expert Systems with Applications

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“…The CBR system of Parvis (1995, 1997), which formalizes the adaptation process with constraints, was applied to the configuration domain. The same approach was used by Ruet and Geneste (2002) in order to assist the design of plant operations, where the CSP technique is used to guide the adaptation phase of the retrieved solution. Several other approaches integrating CBR and CSP were developed, pointing out the effectiveness of this coupling; a detailed state of the art was carried out by Sqalli et al (1999).…”

Section: Related Work Integrating Cbr and Cspmentioning

confidence: 99%

Improvement of online adaptation knowledge acquisition and reuse in case-based reasoning: Application to process engineering design

Reyes

Negny

Robles

et al. 2015

Engineering Applications of Artificial Intelligence

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Interactive adaptation method Online knowledge acquisition Failure diagnosis and repair a b s t r a c t Despite various publications in the area during the last few years, the adaptation step is still a crucial phase for a relevant and reasonable Case Based Reasoning system. Furthermore, the online acquisition of the new adaptation knowledge is of particular interest as it enables the progressive improvement of the system while reducing the knowledge engineering effort without constraints for the expert. Therefore this paper presents a new interactive method for adaptation knowledge elicitation, acquisition and reuse, thanks to a modification of the traditional CBR cycle. Moreover to improve adaptation knowledge reuse, a test procedure is also implemented to help the user in the adaptation step and its diagnosis during adaptation failure. A study on the quality and usefulness of the new knowledge acquired is also driven.As our Knowledge Based Systems (KBS) is more focused on preliminary design, and more particularly in the field of process engineering, we need to unify in the same method two types of knowledge: contextual and general. To realize this, this article proposes the integration of the Constraint Satisfaction Problem (based on general knowledge) approach into the Case Based Reasoning (based on contextual knowledge) process to improve the case representation and the adaptation of past experiences. To highlight its capability, the proposed approach is illustrated through a case study dedicated to the design of an industrial mixing device.

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“…It is generally considered as a key activity (Policastro et al, 2006). Some rules are generally defined for adapting prior solutions to new problems (Ruet & Geneste, 2002). These rules are difficult to define and to use because of the changing context of the different problems. Revision: The solved case has to be revised and is transformed into a revised case or, in other words, a suggested solution is transformed into a confirmed solution. Retention: The CBR system can learn the new case by its incorporation into the case base.The requirements this paper is dealing with are presented in the next section.…”

Section: Models Processes Standards and Knowledge Management (Km) mentioning

confidence: 99%

Case-based reasoning and system design: An integrated approach based on ontology and preference modeling

Bejarano

Coudert²,

Vareilles³

et al. 2014

AIEDAM

Self Cite

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International audienceThis paper addresses the fulfillment of requirements related to case-based reasoning (CBR) processes for system design. Considering that CBR processes are well suited for problem solving, the proposed method concerns the definition of an integrated CBR process in line with system engineering principles. After the definition of the requirements that the approach has to fulfill, an ontology is defined to capitalize knowledge about the design within concepts. Based on the ontology, models are provided for requirements and solutions representation. Next, a recursive CBR process, suitable for system design, is provided. Uncertainty and designer preferences as well as ontological guidelines are considered during the requirements definition, the compatible cases retrieval, and the solution definition steps. This approach is designed to give flexibility within the CBR process as well as to provide guidelines to the designer. Such questions as the following are conjointly treated: how to guide the designer to be sure that the requirements are correctly defined and suitable for the retrieval step, how to retrieve cases when there are no available similarity measures, and how to enlarge the research scope during the retrieval step to obtain a sufficient panel of solutions. Finally, an example of system engineering in the aeronautic domain illustrates the proposed method. A testbed has been developed and carried out to evaluate the performance of the retrieval algorithm and a software prototype has been developed in order to test the approach. The outcome of this work is a recursive CBR process suitable to engineering design and compatible with standards. Requirements are modeled by means of flexible constraints, where the designer preferences are used to express the flexibility. Similar solutions can be retrieved even if similarity measures between features are not available. Simultaneously, ontological guidelines are used to guide the process and to aid the designer to express her/his preferences

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Search and Adaptation in a Fuzzy Object Oriented Case Base

Cited by 11 publications

References 8 publications

A fuzzy CBR technique for generating product ideas

A fuzzy CBR technique for generating product ideas

Improvement of online adaptation knowledge acquisition and reuse in case-based reasoning: Application to process engineering design

Case-based reasoning and system design: An integrated approach based on ontology and preference modeling

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