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Journal articles on the topic 'Knowledge acquisition (Expert systems) Expert systems (Computer science)'

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Published: 4 June 2021

Last updated: 1 February 2022

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1

Edwards, Marion, and Roger E. Cooley. "Expertise in expert systems: knowledge acquisition for biological expert systems." Bioinformatics 9, no. 6 (1993): 657–65. http://dx.doi.org/10.1093/bioinformatics/9.6.657.

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2

Hwang, Gwo-Jen. "Knowledge acquisition for fuzzy expert systems." International Journal of Intelligent Systems 10, no. 6 (1995): 541–60. http://dx.doi.org/10.1002/int.4550100602.

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3

Hammad, Amin, and Yoshito Itoh. "Knowledge Acquisition for Bridge Design Expert Systems." Computer-Aided Civil and Infrastructure Engineering 8, no. 3 (2008): 211–24. http://dx.doi.org/10.1111/j.1467-8667.1993.tb00206.x.

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4

Broner, Israel, J. Phillip King, and Amnon Nevo. "Structured induction for agricultural expert systems knowledge acquisition." Computers and Electronics in Agriculture 5, no. 2 (1990): 87–99. http://dx.doi.org/10.1016/0168-1699(90)90025-k.

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5

Lightfoot, Jay M. "Expert knowledge acquisition and the unwilling expert: a knowledge engineering perspective." Expert Systems 16, no. 3 (1999): 141–47. http://dx.doi.org/10.1111/1468-0394.00104.

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6

Lu, Stephen C.-Y. "Knowledge map: An approach to knowledge acquisition in developing engineering expert systems." Engineering with Computers 3, no. 2 (1987): 59–68. http://dx.doi.org/10.1007/bf01206303.

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7

POULYMENAKOU, A., T. CORNFORD, and E. WHITLEY. "Knowledge acquisition for organisational problem solving: Developing expert systems and beyond☆." Expert Systems with Applications 5, no. 1-2 (1992): 121–30. http://dx.doi.org/10.1016/0957-4174(92)90101-w.

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8

Kourtz, Peter. "Artificial intelligence: a new tool for forest management." Canadian Journal of Forest Research 20, no. 4 (1990): 428–37. http://dx.doi.org/10.1139/x90-060.

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Articicial intelligence is a new science that deals with the representation, automatic acquisition, and use of knowledge. Artificial intelligence programs attempt to emulate human thought processes such as deduction, inference, language, and visual recognition. The goal of artificial intelligence is to make computers more useful for reasoning, planning, acting, and communicating with humans. Development of artificial intelligence applications involves the integration of advanced computer science, psychology, and sometimes robotics. Of the subfields that artificial intelligence can be broken into, the one of most immediate interest to forest management is expert systems. Expert systems involve encoding knowledge usually derived from an expert in a narrow subject area and using this knowledge to mimic his decision making. The knowledge is represented usually in the form of facts and rules, involving symbols such as English words. At the core of these systems is a mechanism that automatically searches for and pieces together the facts and rules necessary to solve a specific problem. Small expert systems can be developed on common microcomputers using existing low-cost commercial expert shells. Shells are general expert systems empty of knowledge. The user merely defines the solution structure and adds the desired knowledge. Larger systems usually require integration with existing forestry data bases and models. Their development requires either the relatively expensive expert system development tool kits or the use of one of the artificial intelligence development languages such as lisp or PROLOG. Large systems are expensive to develop, require a high degree of skill in knowledge engineering and computer science, and can require years of testing and modification before they become operational. Expert systems have a major role in all aspects of Canadian forestry. They can be used in conjunction with conventional process models to add currently lacking expert knowledge or as pure knowledge-based systems to solve problems never before tackled. They can preserve and accumulate forestry knowledge by encoding it. Expert systems allow us to package our forestry knowlege into a transportable and saleable product. They are a means to ensure consistent application of policies and operational procedures. There is a sense of urgency associated with the integration of artificial intelligence tools into Canadian forestry. Canada must awaken to the potential of this technology. Such systems are essential to improve industrial efficiency. A possible spin-off will be a resource knowledge business that can market our forestry knowledge worldwide. If we act decisively, we can easily compete with other countries such as Japan to fill this niche. A consortium of resource companies, provincial resource agencies, universities, and federal government laboratories is required to advance this goal.

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Yang, Jie, Chenzhou Ye, and Xiaoli Zhang. "An Expert System Shell for Fault Diagnosis." Robotica 19, no. 6 (2001): 669–74. http://dx.doi.org/10.1017/s0263574701003460.

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Traditional expert systems for fault diagnosis have a bottleneck in knowledge acquisition, and have limitations in knowledge representation and reasoning. A new expert system shell for fault diagnosis is presented in this paper to develop multiple knowledge models (object model, rules, neural network, case-base and diagnose models) hierarchically based on multiple knowledge. The structure of the expert system shell and the knowledge representation of multiple models are described. Diagnostic algorithms are presented for automatic modeling and hierarchical reasoning. It will be shown that the expert system shell is very effective in building diagnostic expert systems.

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10

Rafea, Ahmed, Ayman El-Dessouki, Hesham Hassan, and Soliman Mohamed. "Development and implementation of a knowledge acquisition methodology for crop management expert systems." Computers and Electronics in Agriculture 8, no. 2 (1993): 129–46. http://dx.doi.org/10.1016/0168-1699(93)90012-p.

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11

Matsatsinis, N. F., M. Doumpos, and C. Zopounidis. "Knowledge acquisition and representation for expert systems in the field of financial analysis." Expert Systems with Applications 12, no. 2 (1997): 247–62. http://dx.doi.org/10.1016/s0957-4174(96)00098-x.

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Scheftic, Carol, and George Darlington Wood. "Student-Teacher: The Learning Machine as an Instructional Device." Journal of Educational Computing Research 4, no. 1 (1988): 57–70. http://dx.doi.org/10.2190/f04u-g1n3-vb0n-qllm.

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This article presents QUERIOUS, an educational tool based on expert system technology, functional analysis techniques, and Socratic method. Using a knowledge acquisition device originally developed to allow specialists to impart their knowledge to expert systems, our tool asks students questions, leads them through functional analysis of a problem and, in effect, induces them to teach the system their solution strategy. An existing prototype is described, and a proposal for further development is presented.

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13

Yan, Hongmei, Yingtao Jiang, Jun Zheng, Bingmei Fu, Shouzhong Xiao, and Chenglin Peng. "The internet-based knowledge acquisition and management method to construct large-scale distributed medical expert systems." Computer Methods and Programs in Biomedicine 74, no. 1 (2004): 1–10. http://dx.doi.org/10.1016/s0169-2607(03)00076-2.

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14

Smedley, Georgia A., and Steve G. Sutton. "Explanation Provision in Knowledge-Based Systems: A Theory-Driven Approach for Knowledge Transfer Designs." Journal of Emerging Technologies in Accounting 1, no. 1 (2004): 41–61. http://dx.doi.org/10.2308/jeta.2004.1.1.41.

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Knowledge-Based Systems (KBS) have been used in industry to free experts from mundane and routine decision making, to produce comparable and consistent decisions, and to retain the expertise of knowledgeable employees who may, for many reasons, leave a company. KBS are also desired to have the capacity to transfer knowledge to less-expert users of such systems. In this paper, Adaptive Character of Thought-Rational (ACT-R) theory is used as a foundation for the design of KBS explanations for the explicit purpose of facilitating knowledge transfer to the user. ACT-R (Anderson 1993) is a theory of cognitive skill acquisition that suggests a learner must first obtain certain facts about a new learning situation (declarative memory pieces) and then convert a series of facts into a set of rules that will produce accurate problem-solving skills (procedural memory pieces). Prior research has examined pieces of the ACT theory in its earlier forms, but no comprehensive tests examining the simultaneous effect of the multiple components have previously been completed. The current study addresses three questions based on ACT-R theory: (1) Can declarative-knowledge-based explanations improve declarative knowledge transfer? (2) Can declarative-knowledge-based explanations improve procedural knowledge transfer? (3) Can procedural-knowledge-based explanations improve procedural knowledge transfer? An experiment employing eight KBS, differing by types of KBS explanation prompts, which were designed to stimulate declarative and/or procedural knowledge transfer, was conducted with 294 accounting information systems students. An analysis of the results provides some support for the use of declarative-based KBS explanations for declarative knowledge transfer, strong support for the use of declarative-based KBS explanations for procedural knowledge transfer, but a lack of support for the use of procedural-based KBS explanations for procedural knowledge transfer. The results suggest that organizations may be able to improve the efficiency and effectiveness of training programs for knowledge workers through the application of KBS that include declarative knowledge-based explanations.

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Wagner, William P., Mohammad K. Najdawi, and Q. B. Chung. "Selection of knowledge acquisition techniques based upon the problem domain characteristics of production and operations management expert systems." Expert Systems 18, no. 2 (2001): 76–87. http://dx.doi.org/10.1111/1468-0394.00159.

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16

Isermann, R., and B. Freyermuth. "Process Fault Diagnosis Based on Process Model Knowledge: Part II—Case Study Experiments." Journal of Dynamic Systems, Measurement, and Control 113, no. 4 (1991): 627–33. http://dx.doi.org/10.1115/1.2896467.

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A computer assisted fault diagnosis system (CAFD) is considered which allows the early detection and localization of process faults during normal operation or on request. It is based on an on-line engineering expert system and consists of an analytical problem solution, a process knowledge base, a knowledge acquisition component and an inference mechanism. The analytic problem solution uses a process parameter estimation, and the detection of process coefficient changes, which are symptoms of process faults. The process knowledge base is comprised of analytical knowledge in the form of process models and heuristic knowledge in the form of fault trees and fault statistics. In the phase of knowledge acquisition the process specific knowledge like theoretical process models, the normal behavior and fault trees is compiled. The inference mechanism performs the fault diagnosis, based on the observed symptoms, the fault trees, fault probabilities and the process history. This is described in Part I. In Part II, case study experiments with a d.c. motor, centrifugal pump, a heat exchanger and an industrial robot show practical results of the model based fault diagnosis.

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Isermann, R., and B. Freyermuth. "Process Fault Diagnosis Based on Process Model Knowledge: Part I—Principles for Fault Diagnosis With Parameter Estimation." Journal of Dynamic Systems, Measurement, and Control 113, no. 4 (1991): 620–26. http://dx.doi.org/10.1115/1.2896466.

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A computer assisted fault diagnosis system (CAFD) is considered which allows the early detection and localization of process faults during normal operation or on request. It is based on an on-line engineering expert system and consists of an analytic problem solution, a process knowledge base, a knowledge acquisition component and an inference mechanism. The analytic problem solution uses a process parameter estimation, and the detection of process coefficient changes, which are symptoms of process faults. The process knowledge base is comprised of analytical knowledge in the form of process models and heuristic knowledge in the form of fault trees and fault statistics. In the phase of knowledge acquisition the process specific knowledge like theoretical process models, the normal behavior and fault trees, is compiled. The inference mechanism performs the fault diagnosis, based on the observed symptoms, the fault trees, fault probabilities and the process history. This is described in Part I. In Part II case study experiments with a d.c. motor, centrifugal pump, a heat exchanger, and an industrial robot show practical results of the model based fault diagnosis.

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18

Gu, Dongxiao, Changyong Liang, Kyung-Sun Kim, Changhui Yang, Wenjuan Cheng, and Jun Wang. "Which is More Reliable, Expert Experience or Information Itself? Weight Scheme of Complex Cases for Health Management Decision Making." International Journal of Information Technology & Decision Making 14, no. 03 (2015): 597–620. http://dx.doi.org/10.1142/s0219622014500424.

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How to obtain valuable knowledge more effectively from historical cases and satisfy the requirements of supporting diagnosis or management decision making is one of the important and challenging issues in the research field of modern historical information management and intelligent decision-making science. In this study, we develop a novel case-based reasoning (CBR) method which is based on information entropy and improved gray systems theory for knowledge acquisition of historical diagnosis decision-making cases. Specially, information entropy for weight determination is introduced into the CBR, as well as a gray system theory combined to support the diagnosis decision making of breast cancer. Based on two different real-world data sets, we conduct experimental studies to compare the performance of the Delphi method and information entropy. We also investigate which combination is best among different weight determination methods and retrieval algorithms. The results suggest that: generally, information entropy is a better approach to weight derivation and better matching effect can be obtained if it is integrated into the retrieval algorithm based on gray system theory rather than Euclidean distance algorithm. Our study can provide a novel approach to obtain weight values of cases, as well as an effective tool to mine valuable decision knowledge from historical cases in public hospitals.

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19

Petit, Maxime, Grégoire Pointeau, and Peter Ford Dominey. "Reasoning based on consolidated real world experience acquired by a humanoid robot." Interaction Studies 17, no. 2 (2016): 248–78. http://dx.doi.org/10.1075/is.17.2.04pet.

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Abstract The development of reasoning systems exploiting expert knowledge from interactions with humans is a non-trivial problem, particularly when considering how the information can be coded in the knowledge representation. For example, in human development, the acquisition of knowledge at one level requires the consolidation of knowledge from lower levels. How is the accumulated experience structured to allow the individual to apply knowledge to new situations, allowing reasoning and adaptation? We investigate how this can be done automatically by an iCub that interacts with humans to acquire knowledge via demonstration. Once consolidated, this knowledge is used in further acquisitions of experience concerning preconditions and consequences of actions. Finally, this knowledge is translated into rules that allow reasoning and planning for novel problem solving, including a Tower of Hanoi scenario. We thus demonstrate proof of concept for an interaction system that uses knowledge acquired from human interactions to reason about new situations.

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Rosário, Cláudio Roberto, Liane Mahlmann Kipper, Rejane Frozza, and Bruna Bueno Mariani. "Methodology for acquisition of collective tacit knowledge used in diagnosis of defect cause in industrial processes." VINE 45, no. 1 (2015): 22–45. http://dx.doi.org/10.1108/vine-03-2013-0013.

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Purpose – The purpose of this paper was to build the MACTAK methodology, which aims to transform collective tacit knowledge into the explicit one using knowledge elicitation techniques, associated to quality tools structured by systemography, represent it in a symbolic language and production rules and model it in two expert systems which can assist the investigation of defect causes during the metal packaging production process. Design/methodology/approach – The method applied in the research was classified as exploratory, because a preliminary study was conducted, to better suit the mapping methodology for eliciting collective tacit knowledge, to the reality which was intended to be known. Through studies and the application of the systemography technique, a methodology for the elicitation of collective tacit knowledge has been developed. It suggests a systematic sequence of activities, to map and transform collective tacit knowledge into the explicit one, on the production process which was studied. Findings – The types of tacit knowledge were mapped and became explicit through the application of the methodology proposed. A knowledge management system was created, as such knowledge was validated by other mechanics during their training on the shop-floor, which resulted in a structure of unique and shared knowledge. They became explicit for being stored in a knowledge base and presented to its users through the expert system. It is concluded that the methodology of acquiring collective tacit knowledge helped on the reduction of rework index by standardizing the way used to investigate the cause of the defect, in the studied company. Research limitations/implications – The MACTAK methodology was developed for exclusive use in industrial processes where the following elements are presented: process, method, environment, raw materials, labor work, measurement and machine. In this method, the detection of the problem occurs from statistical data. Practical implications – The methodology began in August 2010, and in October 2011, obtained as a result a reduction in rework cost equivalent to US$17,780.95. Originality/value – The methodology is unique, as it refers to the systematic use of knowledge acquisition techniques and tools of quality, and the methodology has a characteristic of direct application in manufacturing processes. The beneficiaries, in this case, are mechanicals of production and quality inspectors that work at the operation level in the company. For the organizational and tactical level, the beneficiaries are engineers of production.

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21

Poblete, Carlos Muñoz, Francisco Vargas Parra, Jaime Bustos Gomez, Millaray Curilem Saldias, Sonia Salvo Garrido, and Horacio Miranda Vargas. "Fuzzy Logic in Genetic Regulatory Network Models." International Journal of Computers Communications & Control 4, no. 4 (2009): 363. http://dx.doi.org/10.15837/ijccc.2009.4.2453.

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<p>Interactions between genes and the proteins they synthesize shape genetic regulatory networks (GRN). Several models have been proposed to describe these interactions, been the most commonly used those based on ordinary differential equations (ODEs). Some approximations using piecewise linear differential equations (PLDEs), have been proposed to simplify the model non linearities. However they not allways give good results. In this context, it has been developed a model capable of representing small GRN, combining characteristics from the ODE’s models and fuzzy inference systems (FIS). The FIS is trained through an artificial neural network, which forms an Adaptive Nertwork-based Fuzzy Inference System (ANFIS). This network allows to adapt the membership and output functions from the FIS according to the training data, thus, reducing the previous knowledge needed to model the specific phenomenon.<br /> In addition, Fuzzy Logic allows to express their rules through linguistic labels, which also allows to incorporate expert knowledge in a friendly way. The proposed model has been used to describe the Lac Operon in E. Coli and it has been compared with the models already mentioned. The outcome errors due to the training process of the ANFIS network are comparable with those of the models based on ODEs. Additionally, the fuzzy logic approach provides modeling flexibility and knowledge acquisition advantages.</p>

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22

Shafiee, Sara, Anders Haug, Saeedeh Shafiee Kristensen, and Lars Hvam. "Application of design thinking to product-configuration projects." Journal of Manufacturing Technology Management 32, no. 1 (2020): 219–41. http://dx.doi.org/10.1108/jmtm-04-2020-0137.

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PurposeProduct configurators are expert systems that support product customization by defining how predefined entities and their properties may be combined. Developers of configuration systems act as designers, although they do not often recognize that they are performing as such. Moreover, exploring solution spaces is typically not integral to configuration projects, as this task is typically perceived as mapping existing knowledge to the configurator. This article argues that developing configurators may be understood by distinguishing between the problem and solution spaces using design thinking (DT).Design/methodology/approachA multiple-case-study approach with four configuration projects is adopted to study two projects involving DT and compare them to two similar projects not involving DT. Data collection depended on multiple data sources via workshops and semi-structured interviews.FindingsFirst, DT methods and concept–knowledge (C-K) theory are integrated into configuration projects. Second, the application of DT during configurator development is presented through workshops and interviews, which demonstrates the benefits of DT in overcoming existing challenges.Research limitations/implicationsThe case studies demonstrate the successful implementation of DT in developing configurators. However, a limited number of cases in only one company limits the generalizability of the results.Practical implicationsThe framework's individual steps create a structured approach to supporting industrial companies with a toolbox of DT techniques and methods for configuration projects.Originality/valueThe results show that the application of DT to configuration projects can improve user motivation, stakeholder satisfaction and knowledge acquisition.

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23

Brodheim, E. "Knowledge acquisition for expert systems." European Journal of Operational Research 32, no. 1 (1987): 153. http://dx.doi.org/10.1016/0377-2217(87)90281-5.

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Zimmermann, H. J., and Richard Weber. "Expert systems, automating knowledge acquisition." European Journal of Operational Research 39, no. 3 (1989): 351–52. http://dx.doi.org/10.1016/0377-2217(89)90176-8.

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25

Edwards, John S., and Alison L. Kidd. "Knowledge Acquisition for Expert Systems: A Practical Handbook." Journal of the Operational Research Society 40, no. 3 (1989): 303. http://dx.doi.org/10.2307/2583344.

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Edwards, John S. "Knowledge Acquisition for Expert Systems: A Practical Handbook." Journal of the Operational Research Society 40, no. 3 (1989): 303–4. http://dx.doi.org/10.1057/jors.1989.41.

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27

Hyman, William A., Waymon L. Johnston, and Steven Spar. "Knowledge based and expert systems." Computers & Industrial Engineering 15, no. 1-4 (1988): 51–58. http://dx.doi.org/10.1016/0360-8352(88)90063-0.

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28

Pransky, Joanne. "The Pransky interview: Mitchell Weiss, CTO, Seegrid Corporation." Industrial Robot: An International Journal 44, no. 2 (2017): 137–41. http://dx.doi.org/10.1108/ir-01-2017-0012.

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Purpose The purpose of this paper is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry engineer-turned-entrepreneur regarding the evolution, commercialization and challenges of bringing a technological invention to market. Design/methodology/approach The interviewee is Mitchell Weiss, Chief Technology Officer (CTO) for Seegrid Corporation, a manufacturer of stereo vision-guided robots and vehicle control systems. As an accomplished executive of automation and robotics companies, Weiss shares his experiences and industry knowledge, including his first full-time job out of college at Unimation, the world’s first robot company. Findings Weiss received a Bachelor of Science from MIT and a Graduate Certificate in Intellectual Property (IP) from Northeastern University, has taught at Penn State and the University of Pennsylvania and has lectured at MIT. He has served as the Chief Operating Officer at Seegrid Corp.; CTO at Brooks Automation; CTO and Vice President of PRI Automation; President of ProgramMation, Inc.; and Chief Engineer and Co-Founder at United States Robots, Inc. Originality/value Weiss holds 24 patents, is an expert witness in IP litigation, is Vice Chair of ASTM F45 Driverless Automatic Guided Industrial Vehicles and is a member of ANSI/ITSDF B56.5 Safety Standard for Driverless, Automatic Guided Industrial Vehicles and Automated Functions of Manned Industrial Vehicles. He is also one of the co-authors of the 1986 McGraw-Hill book Industrial Robotics: Technology, Programming, and Applications. Weiss has led his high-technology robotic and automation companies to be successful in the installation of worldwide automation systems in semiconductor manufacturing, electronics manufacturing, automotive and warehousing and distribution. His technical achievements in product design, development and production combined with his business expertise in fund-raising, initial public offering and mergers & acquisitions provide companies with a unique, forward-thinking technology roadmap.

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Hamm, Robert M. "Modeling expert forecasting knowledge for incorporation into expert systems." Journal of Forecasting 12, no. 2 (1993): 117–37. http://dx.doi.org/10.1002/for.3980120206.

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30

Shtern, I. B. "Knowledge preparation for expert systems: Computerization of personal knowledge." Cybernetics and Systems Analysis 27, no. 6 (1991): 909–15. http://dx.doi.org/10.1007/bf01246524.

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31

Garner, B. J. "Expert systems: from database to knowledge base." Information and Software Technology 29, no. 2 (1987): 60–65. http://dx.doi.org/10.1016/0950-5849(87)90316-8.

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32

Hertz, David Bendel. "The knowledge engineering basis of expert systems." Expert Systems with Applications 1, no. 1 (1990): 79–84. http://dx.doi.org/10.1016/0957-4174(90)90071-2.

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Dutta, Amitava. "Reasoning with imprecise knowledge in expert systems." Information Sciences 37, no. 1-3 (1985): 3–24. http://dx.doi.org/10.1016/0020-0255(85)90003-9.

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Walton, Joan, M. A. Musen, D. M. Combs, C. D. Lane, E. H. Shortliffe, and L. M. Fagan. "Graphical Access to Medical Expert Systems: III. Design of a Knowledge Acquisition Environment*)." Methods of Information in Medicine 26, no. 03 (1987): 78–88. http://dx.doi.org/10.1055/s-0038-1635497.

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SummaryKnowledge acquisition for expert systems typically is a tedious, iterative process involving long hours of consultation between the domain experts and the computer scientists who serve as knowledge engineers. For well-understood domains, however, it may be possible to facilitate the knowledge acquisition process by allowing domain experts to develop and edit a knowledge base directly. Administration of protocol-directed cancer chemotherapy is such a well-understood application area, and a knowledge acquisition system, called OPAL, has been developed for eliciting chemotherapy-protocol knowledge directly from expert oncologists. OPAL’s knowledge acquisition approach is based on the interactive graphics environment available on current generation workstations. The use of graphics improves the interface by reducing typing, avoiding natural language interpretations, and allowing flexibility in entry sequence. The knowledge in OPAL is displayed using an arrangement of hierarchically related, graphical forms. The position of a particular form in the hierarchy defines the context of the knowledge contained in the form. Intelligent editing programs such as OPAL can streamline the knowledge engineering process for highly structured domains requiring repetitive knowledge entry.

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LaFrance, Marianne. "Stories Knowledge Engineers Tell About Expert Systems." Social Science Computer Review 8, no. 1 (1990): 13–23. http://dx.doi.org/10.1177/089443939000800103.

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Odom, Marcus D., and Hamid Pourjalali. "Knowledge Transfer from Expert Systems vs. Traditional Instruction." Journal of Organizational and End User Computing 8, no. 2 (1996): 14–20. http://dx.doi.org/10.4018/joeuc.1996040102.

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Sriram, D., M. L. Maher, and S. J. Fenves. "Knowledge-based expert systems in structural design." Computers & Structures 20, no. 1-3 (1985): 1–9. http://dx.doi.org/10.1016/0045-7949(85)90047-1.

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Hsu, Ko-Cheng, and Chao-Hsien Chu. "Practical issues in designing knowledge-based expert systems." Computers & Industrial Engineering 17, no. 1-4 (1989): 61–66. http://dx.doi.org/10.1016/0360-8352(89)90037-5.

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Chambers, T. L., and A. R. Parkinson. "Knowledge Representation and Conversion for Hybrid Expert Systems." Journal of Mechanical Design 120, no. 3 (1998): 468–74. http://dx.doi.org/10.1115/1.2829175.

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Many different knowledge representations, such as rules and frames, have been proposed for use with engineering expert systems. Every knowledge representation has certain inherent strengths and weaknesses. A knowledge engineer can exploit the advantages, and avoid the pitfalls, of different common knowledge representations if the knowledge can be mapped from one representation to another as needed. This paper derives the mappings between rules, logic diagrams, decision tables and decision trees using the calculus of truth-functional logic. The mappings for frames have also been derived by Chambers and Parkinson (1995). The logical mappings between these representations are illustrated through a simple example, the limitations of the technique are discussed, and the utility of the technique for the rapid-prototyping and validation of engineering expert systems is introduced. The technique is then applied to three engineering applications, showing great improvements in the resulting knowledge base.

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Batchelor, W. D., R. W. McClendon, and M. E. Wetzstein. "Knowledge engineering approaches in developing expert simulation systems." Computers and Electronics in Agriculture 7, no. 2 (1992): 97–107. http://dx.doi.org/10.1016/s0168-1699(05)80025-0.

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Luo, Hong Qi, and Meng Yu Wang. "Analysis of Expert System in the Field of Machine Design." Advanced Materials Research 479-481 (February 2012): 565–68. http://dx.doi.org/10.4028/www.scientific.net/amr.479-481.565.

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Intelligent CAD system can be formed if integrating the expert system and mechanical CAD. Components of expert system were analyzed, including integrated databases, knowledge bases, knowledge acquisition, inference engine, explanation mechanism and human-computer interface. The model of design-evaluate-redesign was introduced and discussed. Current situation of research on design expert systems was summarized.

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FONG, JOSEPH, and SHI-MING HUANG. "A FRAME MODEL APPROACH FOR EXPERT AND DATABASE SYSTEM INTEGRATION." International Journal of Software Engineering and Knowledge Engineering 09, no. 03 (1999): 369–96. http://dx.doi.org/10.1142/s021819409900022x.

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Expert systems (ES) and database systems (DBS) are major components of information systems and important assets to companies. The development of these systems represent users' knowledge in the application systems. As computer technologies evolve, and as users requirements change, there is a need to upgrade these system to meet the new application requirements. To preserve the knowledge of the existing information systems, a methodology for integrating ES and DBS into an expert database system (EDS) is proposed. The integrated EDS is a knowledge based system (KBS) which derives and stores knowledge in a frame model consisting of a class header, attributes, methods and constraints. It extracts the ES rules and DBS data for an application into coupling classes at run time only. The attributes of the coupling classes are matched with synonyms in a synonym table which resolves their naming and semantic conflicts with user assistance in knowledge acquisition. The resultant EDS is a KBS ready for application development.

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43

Gupta, Ashok. "Knowledge-based expert systems in offshore structural design." Computers & Structures 28, no. 6 (1988): 745–48. http://dx.doi.org/10.1016/0045-7949(88)90414-2.

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44

RICHTER, HEATHER, GREGORY ABOWD, CHRIS MILLER, and HARRY FUNK. "TAGGING KNOWLEDGE ACQUISITION SESSIONS TO FACILITATE KNOWLEDGE TRACEABILITY." International Journal of Software Engineering and Knowledge Engineering 14, no. 01 (2004): 3–19. http://dx.doi.org/10.1142/s0218194004001543.

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Knowledge Acquisition (KA) is important throughout systems development for gathering expert domain knowledge that is incorporated into the requirements and design of a system. There are problems ensuring that accurate and useful knowledge is captured initially, refined as needed, and transferred to later development efforts in a usable format. We present a method, called tagging, for addressing these problems without undue burden on the KA practitioners, along with initial studies to examine the feasibility of real-time tagging and to inform the design of a tool called TAGGER. TAGGER operates by permitting KA discussions to be "tagged" as they happen with concepts and groupings relevant to software development.

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45

Marra, Rose M., and David H. Jonassen. "Transfer Effects of Semantic Networks on Expert Systems: Mindtools at Work." Journal of Educational Computing Research 26, no. 1 (2002): 1–23. http://dx.doi.org/10.2190/wmln-n7jc-21pa-47th.

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Semantic networks and expert systems can support learning and critical thinking as Mindtools. Mindtools are computer-based tools that function as intellectual partners with the learner in order to engage and facilitate critical thinking and higher-order learning. Semantic networks and expert systems in particular are cognitive reflection tools that help learners to build a representation of what they know by designing their own knowledge bases. Semantic networks have been used as a knowledge elicitation tool for expert system construction; however, the effects of using these tools together has never been formally studied. This study examined the effects of building semantic networks on the coherence and utility of expert systems subsequently constructed. Subjects who constructed semantic networks first produced expert systems with significantly more rules and rule types than a control group. The task's intentional ambiguity and the differences in thinking necessary for semantic network and expert system construction may have affected the non-significance of other expert system complexity variables.

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OLSON, JUDITH REITMAN, and HENRY H. RUETER. "Extracting expertise from experts: Methods for knowledge acquisition." Expert Systems 4, no. 3 (1987): 152–68. http://dx.doi.org/10.1111/j.1468-0394.1987.tb00139.x.

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TAKENOUCHI, H., and Y. IWASHITA. "An integrated knowledge representation scheme for expert systems." Expert Systems 4, no. 1 (1987): 38–43. http://dx.doi.org/10.1111/j.1468-0394.1987.tb00410.x.

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Hansen, James V., and William F. Messier. "A knowledge-based expert system for auditing advanced computer systems." European Journal of Operational Research 26, no. 3 (1986): 371–79. http://dx.doi.org/10.1016/0377-2217(86)90139-6.

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Robinson, Brian. "Expert systems in agriculture and long-term research." Canadian Journal of Plant Science 76, no. 4 (1996): 611–17. http://dx.doi.org/10.4141/cjps96-109.

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Expert systems (ES) technology has many existing and potential uses as input into the agricultural production process. ES are simply computer programs that attempt to emulate human expertise for problem solving purposes. This technology is well suited to agricultural problem solving because of its ability to encode existing knowledge within specific application areas and then apply this knowledge within the problem solving process. ES effectively leverage management's input into agricultural production systems by allowing for the assimilation of all available knowledge pertinent to the task at hand. There are many types of agricultural ES and these can generally be categorized into: Crop Management Advisors, Livestock Management Advisors, Planning Systems, Pest Management Sytems, Diagnostic Systems, Conser-vation/Engineering Systems, Process Control Systems and Marketing Advisory Systems. The cost of ES development can be high. Potential projects, therefore, should be subjected to a benefit/cost analysis to ensure that development efforts are well targeted. ES development and agricultural research are complementary processes and should become more coordinated within an integrated knowledge production and implementation cycle. Key words: Expert systems, knowledge, management, agriculture, research

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Rose, Jacob M., Britton A. McKay, Carolyn Strand Norman, and Anna M. Rose. "Designing Decision Aids to Promote the Development of Expertise." Journal of Information Systems 26, no. 1 (2012): 7–34. http://dx.doi.org/10.2308/isys-10188.

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ABSTRACT We investigate whether the use of decision aids that integrate experts' knowledge structures into their designs can effectively promote the acquisition of expert-like knowledge and improve future judgments. Results of two laboratory experiments (one involving 115 senior accounting students and one involving 78 master of accounting students) indicate that: (1) novice users of a decision aid that has an expert knowledge structure embedded into its interface make complex fraud risk assessments that are more similar to experts' risk assessments than do users of aids without expert knowledge structures; (2) users of a decision aid that has an expert knowledge structure embedded into its interface develop knowledge structures that are more similar to the knowledge structures of experts than do users of aids without expert knowledge structures; (3) knowledge structures mediate the relationship between decision aid design and judgment performance; and (4) novices develop expertise through decision aid use even when they are not instructed to learn from the decision aid.

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