Incremental acquisition of search knowledge

Beydoun, Ghassan; Hoffmann, Achim

doi:10.1006/ijhc.1999.0338

Cited by 19 publications

(6 citation statements)

References 35 publications

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“…In (Beydoun and Hoffmann, 2000), Beydoun and Hoffmann presented an approach to incrementally capture search knowledge in general based on collecting expert's justifications for their decisions. In this paper, this approach is applied to the tolerancing problem in mechanical design following the method outlined below.…”

Section: Acquiring Design Knowledgementioning

confidence: 99%

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A dimensional tolerancing knowledge management system using Nested Ripple Down Rules (NRDR)

Hamade

Moulianitis

D'Addonna

et al. 2010

Engineering Applications of Artificial Intelligence

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This paper proposes to use a knowledge acquisition (KA) approach based on Nested Ripple Down Rules (NRDR) to assist in mechanical design focusing on dimensional tolerancing. A knowledge approach to incrementally model expert design processes is implemented. The knowledge is acquired in the context of its use, which substantially supports the KA process. The knowledge is captured which human designers utilize in order to specify dimensional tolerances on shafts and mating holes in order to meet desired classes of fit as set by relevant engineering standards in order to demonstrate the presented approach. The developed dimensional tolerancing knowledge management system would help mechanical designers become more effective in the time-consuming tolerancing process of their designs in the future. Disciplines Physical Sciences and Mathematics Publication DetailsHamade, R. This paper proposes to use a knowledge acquisition (KA) approach based on Nested Ripple Down Rules (NRDR) to assist in mechanical design focusing on dimensional tolerancing. A knowledge approach to incrementally model expert design processes is implemented. The knowledge is acquired in the context of its use, which substantially supports the KA process. The knowledge is captured which human designers utilize in order to specify dimensional tolerances on shafts and mating holes in order to meet desired classes of fit as set by relevant engineering standards in order to demonstrate the presented approach. The developed dimensional tolerancing knowledge management system would help mechanical designers become more effective in the time-consuming tolerancing process of their designs in the future.

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Section: Acquiring Design Knowledgementioning

confidence: 99%

“…This representation should be adaptable easily for various design tasks. To address these issues the RDR (Beydoun and Hoffmann, 2000) is extended with the ability to accommodate domain specific primitives during the knowledge acquisition process. 4.…”

Section: Requirements For a Ka Environmentmentioning

confidence: 99%

A dimensional tolerancing knowledge management system using Nested Ripple Down Rules (NRDR)

Hamade

Moulianitis

D'Addonna

et al. 2010

Engineering Applications of Artificial Intelligence

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“…When the expert enters a new rule r, he/she chooses the conditions of r from the so-called 'difference list' [22]. This list contains attributes satisfied by the case which triggered the addition of r, and it excludes all attributes satisfied by any of the cases covered by the parent of r. In [23], Beydoun and Hoffmann presented an approach to incrementally capture search knowledge in general based on collecting expert's justifications for their decisions. In this paper, we apply this approach to the tolerancing problem in mechanical design proposing a system architecture that is outlined below.…”

Section: The Knowledge Acquisition Toolmentioning

confidence: 99%

Automating dimensional tolerancing using Ripple down Rules (RDR)

Beydoun

Hoffmann

Hamade

2010

Expert Systems with Applications

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We propose to use a knowledge based approach to assist in mechanical design focusing on dimensional tolerancing. To illustrate our approach, we capture the knowledge which human designers utilize in order to specify dimensional tolerances on shafts and mating holes in order to meet desired classes of fit as set by relevant engineering standards. The software system we developed would help mechanical designers become more effective in the time-consuming dimensioning and tolerancing process of their designs in the future. In doing this, the paper makes a twofold contribution to the field of knowledge acquisition: firstly, interface was adjusted to receive mathematical functions with their specifications prior and during the KA process to propose an approach to exploit relationships among several classes with respect to certain numerical features of the cases in order to accelerate the convergence of the RDR knowledge acquisition process by generating artificial cases which are likely to trigger the addition of exception rules. Secondly, it introduces the above problem domain of determining suitable tolerances for mechanical parts in a design as a knowledge acquisition problem.

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“…Nested RDR (NRDR) (Beydoun & Hoffmann, 1997, 2000) allows multiple SCRDR trees to be combined into a hierarchical conceptual structure. The initial work was concerned with capturing search knowledge and allows concepts and rules at various levels of abstraction to be defined and reused.…”

Section: Variations Extensions and Implementationsmentioning

confidence: 99%

Two decades of Ripple Down Rules research

Richards

2009

The Knowledge Engineering Review

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Ripple Down Rules (RDR) were developed in answer to the problem of maintaining medium to large rule-based knowledge systems. Traditional approaches to knowledge-based systems gave little thought to maintenance as it was expected that extensive upfront domain analysis involving a highly trained specialist, the knowledge engineer, and the time-poor domain expert would produce a complete model capturing what was in the expert’s head. The ever-changing, contextual and embrained nature of knowledge were not a part of the philosophy upon which they were based. RDR was a paradigm shift, which made knowledge acquisition and maintenance one and the same thing by incrementally acquiring knowledge as domain experts directly interacted with naturally occurring cases in their domain. Cases played an integral part of the acquisition process by motivating the capture of new knowledge, framing the context in which new knowledge would apply and ensuring that previously correctly classified cases remained so by requiring that the classification of the new case distinguish it from the system’s classification and be justified by features of the new case. RDR has moved beyond its first representation which handled single classification tasks within the domain of pathology to support multiple conclusions across a wide range of domains such as help-desk support, email classification and RoboCup and problem types including configuration, simulation, planning and natural language processing. This paper reviews the history of RDR research over the past two decades with a view to its future.

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Incremental acquisition of search knowledge

Cited by 19 publications

References 35 publications

A dimensional tolerancing knowledge management system using Nested Ripple Down Rules (NRDR)

A dimensional tolerancing knowledge management system using Nested Ripple Down Rules (NRDR)

Automating dimensional tolerancing using Ripple down Rules (RDR)

Two decades of Ripple Down Rules research

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