Educating knowledge engineers on knowledge acquisition

Liebowìtz, Jay

doi:10.1109/dmisp.1993.248629

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…Therefore, the first and main obstacle to knowledge acquisition is the knowledge engineering paradox (Liebowitz, 1993). The knowledge and skills that constitute expertise in a particular domain are hidden in the head of experts in a tacit form.…”

Section: Information Biasmentioning

confidence: 99%

Knowledge acquisition for decision support systems on an electronic assembly line

Gebus

Leiviskä

2009

Expert Systems with Applications

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Section: Information Biasmentioning

confidence: 99%

Knowledge acquisition for decision support systems on an electronic assembly line

Gebus

Leiviskä

2009

Expert Systems with Applications

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“…One of the first expert systems was MYCIN in 1974, which In the above selection, McCarthy describes an engineering mindset that potentially excludes some relevant human factors, although many of today's engineers are apparently far more sensitive and responsive to context and user concerns. An assortment of academic programs and courses explicitly labeled as "knowledge engineering" arose in higher education institutions in several Western nations in the 1980s and early 90s (Liebowitz, 1993), such as in the University of Maryland at College Park. A number of the individuals selected by universities and corporations for training in knowledge engineering had backgrounds in the humanities and social sciences, at least temporarily infusing needed ethnographic talent and insight into knowledge-related pursuits (Mykytyn, Mykytyn, & Raja, 1994).…”

Section: Visions Guiding Early Expert System Developmentmentioning

confidence: 99%

Expert Systems and Knowledge-Based Engineering (1984-1991): Implications for Instructional Systems Research

Oravec

2014

IJDL

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Expert system technologies are varieties of artificial intelligence (AI) approaches in which decision-making knowledge is codified and modeled. This design case has the challenging task of characterizing this set of technologies during a particularly important period in its development (1984-1991), with an emphasis on a particular system that was used in food production environments by Campbell Soup. It analyzes the social and research impacts of early, pioneering information elicitation and processing strategies that focused on the distillation of the knowledge or know-how of individuals construed as experts in particular arenas, approaches broadly labeled as “knowledge-based engineering” (KBE). Widely-publicized notions of “thinking machines” and “canned experts” provided motivation for a good deal of early expert systems development (Feigenbaum & McCorduck, 1986), with accusations of “hype” often levied (Blair, 2002). This article historically situates these technological strategies in the period from 1984 though 1991, then links them with current instructional systems approaches that more fully involve collaborative elements as well as contextual perspectives. The motivation for this article is to explore how larger technological and social trends and assumptions can influence particular research efforts, especially in the richly interdisciplinary area of information systems. The article also explores the circumstances and consequences of “failures” of system development, with expert systems providing widely-discussed exemplars (Gill, 1995; Oravec & Plant, 1992). This article is rooted in the assumption that historically-informed perspectives can provide some underpinnings to the building of humane and sustainable research projects, particularly in areas that have human subjects and volatile contexts as essential elements. This article also addresses the continuing legacy of university curricula and business training initiatives that were shaped to accommodate expert system and KBE approaches in past decades. Discourse about human expertise generated by expert system efforts in 1984 through 1991 holds insights for current research and development, as well as signals potential sources of dysfunction of, and opposition to, future instructional system initiatives.

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“…First and main obstacle is the knowledge engineering paradox (Liebowitz, 1993). Knowledge and skills that constitute expertise in a particular domain is tacit.…”

Section: Knowledge Acquisitionmentioning

confidence: 99%

Defect-Related Knowledge Acquisition for Decision Support Systems in Electronics Assembly

Gebus¹,

Leiviskä²

2007

Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics

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Real-time process control and production optimization are extremely challenging areas. Traditional approaches often lack in robustness or reliability when dealing with incomplete, inaccurate, or simply irrelevant data. This is a major problem when building decision support systems especially in electronics manufacturing, where blind feature extraction and data mining methods on large databases are common. Performance of these methods can be drastically increased when combined with knowledge or expertise of the process. This paper describes how defect-related knowledge on an electronic assembly line can be integrated in the decision making process at an operational and organizational level. It focuses in particular on the acquisition of shallow knowledge concerning everyday human interventions on the production lines, as well as on the conceptualization and factory wide sharing of the resulting defect information. Software with dedicated interfaces has been developed for that purpose. Semi-automatic knowledge acquisition from the production floor and generation of comprehensive reports for the quality department resulted in an improvement of the usability, usage, and usefulness of the decision support system.

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Educating knowledge engineers on knowledge acquisition

Cited by 4 publications

References 6 publications

Knowledge acquisition for decision support systems on an electronic assembly line

Knowledge acquisition for decision support systems on an electronic assembly line

Expert Systems and Knowledge-Based Engineering (1984-1991): Implications for Instructional Systems Research

Defect-Related Knowledge Acquisition for Decision Support Systems in Electronics Assembly

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