Opportunistic Adaptation Knowledge Discovery

Badra, Fadi; Cordier, Amélie; Lieber, Jean

doi:10.1007/978-3-642-02998-1_6

Cited by 19 publications

(9 citation statements)

References 5 publications

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“…Nevertheless, the major drawback is the number of iterations that the adaptation can necessitate. Badra et al (2009) graphically sum up these characteristics in Fig. 3.…”

Section: Adaptation Knowledge Acquisitionmentioning

confidence: 97%

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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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“…Nevertheless, the major drawback is the number of iterations that the adaptation can necessitate. Badra et al (2009) graphically sum up these characteristics in Fig. 3.…”

Section: Adaptation Knowledge Acquisitionmentioning

confidence: 97%

“…3. Acquisition times for the adaptation knowledge (Badra et al, 2009). Table 1 CBR and CSP feature's comparison.…”

Section: The Reasons Of the Couplingmentioning

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

View full text Add to dashboard Cite

show abstract

“…In the second version of Taaable, which implements the approach proposed in [2], users may give some feedback on substitutions for adapting recipes. If a proposition of substitution is judged irrelevant by the user, an interface allows to guide the system for repairing the adaptation.…”

Section: Cbr Systemsmentioning

confidence: 99%

“…Like [6], [2] uses an opportunistic approach for AK acquisition. In the second version of Taaable, which implements the approach proposed in [2], users may give some feedback on substitutions for adapting recipes.…”

Section: Cbr Systemsmentioning

confidence: 99%

Man-machine collaboration to acquire cooking adaptation knowledge for the TAAABLE case-based reasoning system

Cordier

Gaillard

Nauer

2012

Proceedings of the 21st International Conference on World Wide Web

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This paper shows how humans and machines can better collaborate to acquire adaptation knowledge (AK) in the framework of a case-based reasoning (CBR) system whose knowledge is encoded in a semantic wiki. Automatic processes like the CBR reasoning process itself, or specific tools for acquiring AK are integrated as wiki extensions. These tools and processes are combined on purpose to collect AK. Users are at the center of our approach, as they are in a classical wiki, but they will now benefit from automatic tools for helping them to feed the wiki. In particular, the CBR system, which is currently only a consumer for the knowledge encoded in the semantic wiki, will also be used for producing knowledge for the wiki. A use case in the domain of cooking is given to exemplify the man-machine collaboration.

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“…In this last case, the modifications are cancelled and a notification is sent to the author. An complementary approach could be to implement some user interactions, as it is for example done in [8], [5].…”

Section: Tests Systemmentioning

confidence: 99%