Device understanding and modeling for diagnosis

Abu‐Hanna, Ameen; Benjamins, Richard; Jansweijer, W.N.H.

doi:10.1109/64.79706

Cited by 44 publications

(8 citation statements)

References 5 publications

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“…After working with the Cover and Differentiate [8] model of problem solving for 7 man-months and following a structured prototyping approach, we realized that important behaviors and representations could not be captured by the model. Instead, for the purpose of guiding the acquisition phase and contributing to meeting the desired target system and environment characteristics, we found that the diagnosis method based on functional design models [1] was more appropriate. The whole re-design, re-adjustment, and reimplementation of the prototype occupied approximately two man-months 13 . In the Schedule project we have not yet performed a detailed evaluation of the "design by decomposition" model which is embedded in the prototype problem solving system that has been constructed so far.…”

Section: Selecting the Wrong Model May Be Costlymentioning

confidence: 98%

“…According to [1] schematics about the processor boards under focus and documentations regarding the board's design and functional specification can fulfill the roles required by this method. This knowledge is available to TroTelC's domain expert.…”

Section: Multiple Generic Models May Applymentioning

confidence: 99%

“…For example, in the TroTelC project, we initially identified the following models as potential candidates: Heuristic Classification [5], Cover and Differentiate [8], Hierarchical Diagnosis [10], Model-based Diagnosis based on functional design models [1] (which we call design-functionality-based method throughout the text), Systematic Diagnosis, and Heuristic Diagnosis [2]. All knowledge requirements of the candidate models were satisfied in the troubleshooting task environment:…”

Section: Multiple Generic Models May Applymentioning

confidence: 99%

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Generic Models and Their Support in Modeling Problem Solving Behavior

Rademakers

Vanwelkenhuysen

1993

Second Generation Expert Systems

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Abstract. Generic models have received widespread attention in knowledge based systems research (KBS) as an important aid in the process of modeling "problem solving behavior". However, little empirical evidence has been presented which justifies this role. In this article, we analyze some of our experiences with using generic models of problem solving in industrial projects. In these projects, we have relied upon generic models to support the acquisition and modeling process as advocated by current modeling methodologies. Based on these experiences, we argue that there is a real danger that practitioners might be misleaded by inappropriate generic models because current modeling methodologies do not sufficiently address the issues related to selecting, refining and instantiating generic models. To avoid this costly endeavor, we argue that modeling methodologies should emphasize more the nature of the domain, the work that people do, the role of the environment and an active role of the expert. We also emphasize the importance of a tight coupling between the modeling activity and validation. Validation should be carried out with respect to the expert source and the contribution of each model construct towards the intended goal of the models. The paper concludes with remarks on a framework that we call "validation through remodeling" .

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Section: Selecting the Wrong Model May Be Costlymentioning

confidence: 98%

Section: Multiple Generic Models May Applymentioning

confidence: 99%

Section: Multiple Generic Models May Applymentioning

confidence: 99%

See 1 more Smart Citation

Generic Models and Their Support in Modeling Problem Solving Behavior

Rademakers

Vanwelkenhuysen

1993

Second Generation Expert Systems

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“…GDE uses minimal entropy Ž as a measure to minimize the expected number of tests s additional observa-. 2 tions . FAULTY minimizes the estimated number of tests based on a variety of balanced global factors.…”

Section: Assumptions In System-environment Interactionmentioning

confidence: 98%

The role of assumptions in knowledge engineering

Fensel

Benjamins

1998

Int. J. Intell. Syst.

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Problem‐solving methods are means of describing the inference process of knowledge‐based systems. In recent years, a number of these problem‐solving methods have been identified that can be reused for building new systems. However, problem‐solving methods require specific types of domain knowledge and introduce specific restrictions on the tasks that can be solved by them. These requirements and restrictions are assumptions that play a key role in the reuse of problem‐solving methods, in the acquisition of domain knowledge, and in the definition of the problem that can be tackled by knowledge‐based systems. In this paper we discuss the different roles assumptions play in the development of knowledge‐based systems and provide a survey of assumptions used in diagnostic problem solving. We show how such assumptions introduce targets and bias for goal‐driven machine learning and knowledge discovery techniques. © 1998 John Wiley & Sons, Inc.

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“…The most difficult point concerns the building of the ontology. Examples of using a model ontology construct to guide the diagnosis already exist in the domain of model-based reasoning, for example [2]. We have also succeeded in constructing ontology for developing medical diagnosis expert systems [6,10].…”

Section: Framework For Future Experimentsmentioning

confidence: 99%

A framework for building hypertext based diagnostic systems

Morizet‐Mahoudeaux

Terray²,

Brunie³

et al.

SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218)

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Among the artificial intelligence techniques useful in diagnosis the integrated diagnostic techniques are often presented as the most efficient to resolve practical problems. This approach may lack however some flexibility to be applied in many application domains, when the role played by users takes an important part of the diagnosis process. Hypertext systems offer now an interesting approach whenever users' interactions are important. However, to fulfil the requirements imposed by practical and professional diagnosis, hypertext systems must satisfy constraints, which generally are not considered for common applications of hypertext. They are, for example, the ability of offering different projections of the same document; effective automatic navigation tools, and a well formulated representation of involved knowledge concepts. This paper presents a framework to build hypertext system-based diagnosis. First, we propose a definition of hypertext systems, which is more appropriate to account for the structural properties, which exist in any document. These structures become perceptible by the means of projections. A projection is decomposed into two steps: an interpretation operation, which is the computation of the document, and an instantiation operation, which is the transformation of the digital document into a perceptible document. Second, we show that by separating nodes content from hypertext documents structures, it becomes possible to implement efficient tools for document reading and synthesis. Last we refer to a model ontology building method, which we use for building domain dependant diagnosis ontology to model hypertext-system based diagnosis. The project of building a hypertext-based system for aiding in the acoustic and vibration diagnosis of rotating machines is finally used to discuss a possible application of the system.

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Device understanding and modeling for diagnosis

Cited by 44 publications

References 5 publications

Generic Models and Their Support in Modeling Problem Solving Behavior

Generic Models and Their Support in Modeling Problem Solving Behavior

The role of assumptions in knowledge engineering

A framework for building hypertext based diagnostic systems

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