Adaptation using constraint satisfaction techniques

Purvis, Lisa; Pu, Pearl

doi:10.1007/3-540-60598-3_26

Cited by 32 publications

(14 citation statements)

References 10 publications

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“…They relate their work to CBR integrations with other types of model-based reasoning, which are described below. Purvis and Pu (1995) integrate CBR with CSP in a different way. They worked initially on assembly planning and configuration design problems, but generalize their approach to any problem that can be formalized as a CSP (Purvis 1998).…”

Section: Research In Integrating Cbr With Other Reasoning Paradigmsmentioning

confidence: 99%

Integrating Case‐Based and Rule‐Based Reasoning to Meet Multiple Design Constraints

Marling

Petot

Sterling

1999

Computational Intelligence

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Although case-based reasoning (CBR) was introduced as an alternative to rule-based reasoning (RBR), there is a growing interest in integrating it with other reasoning paradigms, including RBR. New hybrid approaches are being piloted to achieve new synergies and improve problem-solving capabilities. In our approach to integration, CBR is used to satisfy multiple numeric constraints, and RBR allows the performance of "what if" analysis needed for creative design.The domain of our investigation is nutritional menu planning. The task of designing nutritious, yet appetizing, menus is one at which human experts consistently outperform computer systems. Tailoring a menu to the needs of an individual requires satisfaction of multiple numeric nutrition constraints plus personal preference goals and aesthetic criteria.We first constructed and evaluated independent CBR and RBR menu planning systems, then built a hybrid system incorporating the strengths of each system. The hybrid outperforms either single strategy system, designing superior menus, while synergistically providing functionality that neither single strategy system could provide. In this paper, we present our hybrid approach, which has applicability to other design tasks in which both physical constraints and aesthetic criteria must be met.

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Section: Research In Integrating Cbr With Other Reasoning Paradigmsmentioning

confidence: 99%

Integrating Case‐Based and Rule‐Based Reasoning to Meet Multiple Design Constraints

Marling

Petot

Sterling

1999

Computational Intelligence

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“…The design with the highest match to the requirements is the new design. Purvis & Pu (1995) present adaptation as a constraint satisfaction problem. The design cases are represented as constraint satisfaction problems, where the design requirements are the constraints and the design is the solution.…”

Section: Reuse and Revision In Cbrmentioning

confidence: 99%

Retrieval, reuse, revision and retention in case-based reasoning

Mántaras

McSherry

Bridge

et al. 2005

The Knowledge Engineering Review

441

185

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Case-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision, and retention. R. LÓPEZ DE MÁNTARAS ET AL.

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“…Then, the appropriate constraint model (the one defined for the previous part of the solution space) can be selected and used in order to adapt the case and thus to terminate design. This association of tools has been studied by some authors, such as Purvis and Pu (1995), Inakoshi et al (2001), Ruet and Geneste (2002), Lopez (2003) and Roldan et al (2010).…”

Section: Aiding Decision With Cbr Then Cspmentioning

confidence: 99%

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

Vareilles

Aldanondo

Boisse

et al. 2012

Engineering Applications of Artificial Intelligence

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To cite this version:Élise Vareilles, Michel Aldanondo, Aurélien Codet de Boisse, Thierry Coudert, Paul Gaborit, et al.. How to take into account general and contextual knowledge for interactive aiding design: Towards the coupling of CSP and CBR approaches. Engineering Applications of Artificial Intelligence, Elsevier, 2012Elsevier, , vol. 25, pp. 31 -47. 10.1016Elsevier, /j.engappai.2011 Any correspondence concerning this service should be sent to the repository administrator: staff-oatao@inp-toulouse.fr a b s t r a c tThe goal of this paper is to show how it is possible to support design decisions with two different tools relying on two kinds of knowledge: case-based reasoning operating with contextual knowledge embodied in past cases and constraint filtering that operates with general knowledge formalized using constraints. Our goals are, firstly to make an overview of existing works that analyses the various ways to associate these two kinds of aiding tools essentially in a sequential way. Secondly, we propose an approach that allows us to use them simultaneously in order to assist design decisions with these two kinds of knowledge. The paper is organized as follows. In the first section, we define the goal of the paper and recall the background of case-based reasoning and constraint filtering. In the second section, the industrial problem which led us to consider these two kinds of knowledge is presented. In the third section, an overview of the various possibilities of using these two aiding decision tools in a sequential way is drawn up. In the fourth section, we propose an approach that allows us to use both aiding decision tools in a simultaneous and iterative way according to the availability of knowledge. An example dealing with helicopter maintenance illustrates our proposals.

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Adaptation using constraint satisfaction techniques

Cited by 32 publications

References 10 publications

Integrating Case‐Based and Rule‐Based Reasoning to Meet Multiple Design Constraints

Integrating Case‐Based and Rule‐Based Reasoning to Meet Multiple Design Constraints

Retrieval, reuse, revision and retention in case-based reasoning

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

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