Augmenting knowledge acquisition processes of expert systems with human performance modeling techniques

1988 ◽  
Vol 18 (3) ◽  
pp. 467-472
Author(s):  
M.S. McCoy ◽  
R.R. Levary
Keyword(s):  
Expert Systems ◽  
Knowledge Acquisition ◽  
Human Performance ◽  
Performance Modeling ◽  
Modeling Techniques ◽  
Acquisition Processes

Case research on knowledge acquisition: observations and lessons

1991 ◽  
Vol 6 (2) ◽  
pp. 97-120 ◽  
Author(s):  
Christine Chan ◽  
Izak Benbasat
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
System Development ◽  
Relevant Literature ◽  
System Construction ◽  
Organizational Setting ◽  
Knowledge Engineer ◽  
Case Research ◽  
Acquisition Processes

AbstractExpert systems are being built despite the widely acknowledged problem of acquiring knowledge from experts. This study attempts to understand how knowledge acquisition is conducted in practice by investigating three expert system development projects. A CASE research methodology is adopted, and data is collected through unobtrusive observation, from taped protocols of knowledge acquisition sessions, retrospective interviews with the participants involved, and deliverables produced. The variables examined include the problem domain, the domain expert, the knowledge engineer, the knowledge acquisition process, the expert system construction process, potential users, organizational setting, and the expert system itself. The knowledge acquisition processes for three expert systems in the domains of law of negligence, telephone line fault diagnosis, and wastewater treatment have been examined. By juxtaposing the observations drawn with findings from the relevant literature, the study makes prescriptive suggestions on considerations and techniques for future acquisition efforts, and provides data for hypothesis generation in further research.

An Application of Human Performance Modeling to the Evaluation of Advanced User Interface Features

2000 ◽  
Vol 44 (37) ◽  
pp. 650-653 ◽  
Author(s):  
Tom Carolan ◽  
Shelly Scott-Nash ◽  
Kevin Corker ◽  
David Kellmeyer
Keyword(s):  
Human Performance ◽  
Performance Modeling ◽  
Usability Study ◽  
Design Alternatives ◽  
Complementary Approach ◽  
Advanced User ◽  
Ongoing Work ◽  
Modeling Techniques ◽  
Micro Analysis ◽  
The Impact

Human performance modeling provides a complementary approach to human usability testing methods for evaluating the impact of advanced interface features on operator performance under a variety of conditions and design alternatives. This paper describes ongoing work performed by Micro Analysis & Design, Inc. (MA&D) to apply human performance modeling techniques to support usability study and system design objectives for an advanced workstation.

Knowledge Acquisition for Expert Systems in Construction.

1987 ◽  
Author(s):  
E. G. Trimble ◽  
R. J. Allwood ◽  
A. E. Bryman
Keyword(s):  
Expert Systems ◽  
Knowledge Acquisition

Knowledge Acquisition for Expert Systems in Construction.

1986 ◽  
Author(s):  
E. G. Trimble ◽  
R. J. Allwood ◽  
A. E. Bryman
Keyword(s):  
Expert Systems ◽  
Knowledge Acquisition

scholarly journals Knowledge Acquisition and Representation for High-Performance Building Design: A Review for Defining Requirements for Developing a Design Expert System

2021 ◽  
Vol 13 (9) ◽  
pp. 4640
Author(s):  
Seung-Yeoun Choi ◽  
Sean-Hay Kim
Keyword(s):  
Decision Support ◽  
Expert System ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Data Analytics ◽  
High Performance ◽  
Problem Definition ◽  
Design Expert ◽  
Decision Support Process ◽  
High Performance Building

New functions and requirements of high performance building (HPB) being added and several regulations and certification conditions being reinforced steadily make it harder for designers to decide HPB designs alone. Although many designers wish to rely on HPB consultants for advice, not all projects can afford consultants. We expect that, in the near future, computer aids such as design expert systems can help designers by providing the role of HPB consultants. The effectiveness and success or failure of the solution offered by the expert system must be affected by the quality, systemic structure, resilience, and applicability of expert knowledge. This study aims to set the problem definition and category required for existing HPB designs, and to find the knowledge acquisition and representation methods that are the most suitable to the design expert system based on the literature review. The HPB design literature from the past 10 years revealed that the greatest features of knowledge acquisition and representation are the increasing proportion of computer-based data analytics using machine learning algorithms, whereas rules, frames, and cognitive maps that are derived from heuristics are conventional representation formalisms of traditional expert systems. Moreover, data analytics are applied to not only literally raw data from observations and measurement, but also discrete processed data as the results of simulations or composite rules in order to derive latent rule, hidden pattern, and trends. Furthermore, there is a clear trend that designers prefer the method that decision support tools propose a solution directly as optimizer does. This is due to the lack of resources and time for designers to execute performance evaluation and analysis of alternatives by themselves, even if they have sufficient experience on the HPB. However, because the risk and responsibility for the final design should be taken by designers solely, they are afraid of convenient black box decision making provided by machines. If the process of using the primary knowledge in which frame to reach the solution and how the solution is derived are transparently open to the designers, the solution made by the design expert system will be able to obtain more trust from designers. This transparent decision support process would comply with the requirement specified in a recent design study that designers prefer flexible design environments that give more creative control and freedom over design options, when compared to an automated optimization approach.

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