How to take into account general and contextual knowledge for interactive aiding design: Towards the coupling of CSP and CBR approaches

Vareilles, ílise; Aldanondo, Michel; Boisse, Aurélien Codet de; Coudert, Thierry; Gaborit, Paul; Geneste, Laurent

doi:10.1016/j.engappai.2011.09.002

Cited by 9 publications

(8 citation statements)

References 19 publications

(17 reference statements)

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“…Third, constraint satisfaction problem filtering tools may be used in order to reduce the solution space very early. We have proposed first methods for coupling such CBR and constraint satisfaction problem in Vareilles et al (2012).…”

Section: Resultsmentioning

confidence: 99%

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

Bejarano

Coudert²,

Vareilles³

et al. 2014

AIEDAM

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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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Section: Resultsmentioning

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

View full text Add to dashboard Cite

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“…However, Vareilles et al (2012) have proposed using them not successively but iteratively to reach a deeper coupling. We have not implemented such an iterative approach because it cannot afford to reach the other objectives of our KBS, i.e.…”

Section: Discussionmentioning

confidence: 98%

“…According to Vareilles et al (2012), only the last two possibilities really deal with knowledge processing. The other ones were more focused on knowledge validation or completeness, which is out of the scope of our study.…”

Section: Discussionmentioning

confidence: 98%

“…Wang et al (2009) proposed an algorithm that integrates the two approaches applied to "online" product configuration: the user inputs his request then the CBR recovers a similar case, which is modified according to conditional constraints. Recently Vareilles et al (2012) have summed up these previous couplings into four possibilities:…”

Section: Related Work Integrating Cbr and Cspmentioning

confidence: 99%

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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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“…Once this identification is made and validated, the generic configuration model is updated with the relevant ETO knowledge. In line with many authors, see Vareilles et al [19] and Felfernig et al [10], we consider the configuration problem as a constraint satisfaction problem (CSP) and use this framework for modeling. We need to point out that our proposals are not only dedicated to the bidding process and can be used in other design activities as well.…”

Section: Introductionmentioning

confidence: 99%

Configuration knowledge modeling: How to extend configuration from assemble/make to order towards engineer to order for the bidding process

Sylla

Guillon

Vareilles

et al. 2018

Computers in Industry

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The bidding process is one of the most important phases for system contractors. A successful bid implies defining and implementing attractive and realistic systems solutions that fulfill customer expectations. An additional challenge arises with the increase in systems diversity resulting from growing customization needs. As a result, for standard customizing offers, bidders find good quality support with configuration software for assemble/make-to-order situations. But when requirements exceed the standard offers, bidders need extended support to fulfill Engineering-to-Order requirements. In this context, this article shows how configuration knowledge models, which support configuration in assemble/make-to-order situations (AMTO), can be extended and used in engineer-to-order situations (ETO). Modeling is achieved assuming that the configuration problem is considered as a constraint satisfaction problem. Six key requirements that differentiate ETO from AMTO are identified and modeling extensions are proposed and discussed. An example illustrates all the contributions.

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How to take into account general and contextual knowledge for interactive aiding design: Towards the coupling of CSP and CBR approaches

Cited by 9 publications

References 19 publications

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

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

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

Configuration knowledge modeling: How to extend configuration from assemble/make to order towards engineer to order for the bidding process

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