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

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Published: 4 June 2021

Last updated: 29 July 2024

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1

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

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2

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

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3

Brooks, H. M. "Knowledge acquisition for expert systems." Information Processing & Management 24, no. 2 (January 1988): 208–9. http://dx.doi.org/10.1016/0306-4573(88)90117-3.

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4

Belkin, Brenda L., and Robert F. Stengel. "Quantitative knowledge acquisition for expert systems." Engineering Applications of Artificial Intelligence 3, no. 4 (December 1990): 271–81. http://dx.doi.org/10.1016/0952-1976(90)90026-i.

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5

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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6

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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7

Hammad, Amin, and Yoshito Itoh. "Knowledge Acquisition for Bridge Design Expert Systems." Computer-Aided Civil and Infrastructure Engineering 8, no. 3 (November 6, 2008): 211–24. http://dx.doi.org/10.1111/j.1467-8667.1993.tb00206.x.

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8

Neale, Ian M. "First generation expert systems: a review of knowledge acquisition methodologies." Knowledge Engineering Review 3, no. 2 (June 1988): 105–45. http://dx.doi.org/10.1017/s0269888900004288.

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AbstractThis paper reviews a wide range of knowledge acquisition techniques in the context of attempts to achieve a systematic methodology. These have been poorly documented by expert system builders, who are often inclined to overvalue textbooks and the ways experts themselves claim they solve problems. No one method has a universal advantage; each has some value. Techniques should be selected to suit the domain, the task, the expert and the knowledge engineer. Knowledge acquisition involves creating a conceptual model of expert knowledge and reasoning, from analysis of data elicited by these

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Усамов, И. Р., Б. Ж. Юхигов, and З. А. Магазиева. "EXPERT SYSTEMS SHELL: AN EFFECTIVE KNOWLEDGE ACQUISITION MODEL." Вестник ГГНТУ. Гуманитарные и социально-экономические науки, no. 2(28) (August 26, 2022): 80–90. http://dx.doi.org/10.34708/gstou.2022.61.19.010.

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Экспертная система - это интеллектуальная компьютерная программа, которая использует знания и процедуры вывода для решения проблем, которые считаются достаточно сложными, чтобы для их решения хватило человеческого опыта. С момента внедрения первых экспертных систем в начале 1970-х годов и последовавших за этим успехов в сочетании с преимуществами, которые человечество получило от этих ранних экспертных систем и их преемников, можно было бы ожидать, что пять десятилетий спустя произойдет бум использования экспертных систем для выполнения более специализированных задач в конкретных областях прим

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10

Chan, Christine, and Izak Benbasat. "Case research on knowledge acquisition: observations and lessons." Knowledge Engineering Review 6, no. 2 (June 1991): 97–120. http://dx.doi.org/10.1017/s0269888900005634.

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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 knowled

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11

Gaines, Brian. "Knowledge Acquisition Systems For Rapid Prototyping Of Expert Systems." INFOR: Information Systems and Operational Research 26, no. 4 (January 1988): 256–85. http://dx.doi.org/10.1080/03155986.1988.11732070.

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12

Johnson, N. E. "Knowledge based systems series Vol. 1: Knowledge acquisition for knowledge based systems Vol. 2: Knowledge acquisition tools for expert systems." Knowledge-Based Systems 3, no. 3 (September 1990): 181. http://dx.doi.org/10.1016/0950-7051(91)90030-6.

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13

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

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14

Shangraw, R. F. "Knowledge Acquisition, Expert Systems, and Public Management Decisions." Social Science Microcomputer Review 5, no. 2 (July 1987): 163–73. http://dx.doi.org/10.1177/089443938700500203.

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15

CORDIER, MARIE-ODILE, and CHANTAL REYNAUD. "KNOWLEDGE ACQUISITION TECHNIQUES AND SECOND-GENERATION EXPERT SYSTEMS." Applied Artificial Intelligence 5, no. 3 (January 1991): 209–26. http://dx.doi.org/10.1080/08839519108927926.

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16

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

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17

Diederich, Joachim, Ingo Ruhmann, and Mark May. "KRITON: a knowledge-acquisition tool for expert systems." International Journal of Man-Machine Studies 26, no. 1 (January 1987): 29–40. http://dx.doi.org/10.1016/s0020-7373(87)80033-0.

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18

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

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19

Jin-Ming, Zhou, and Xiong Fan-Lun. "Knowledge Acquisition in Fertilization Expert Systems Building Tool." IFAC Proceedings Volumes 22, no. 12 (September 1989): 53–58. http://dx.doi.org/10.1016/s1474-6670(17)53054-2.

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20

Doyle, J. R. "Probability problems in knowledge acquisition for expert systems." Knowledge-Based Systems 1, no. 2 (March 1988): 114–20. http://dx.doi.org/10.1016/0950-7051(88)90006-8.

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21

Gappa, Ute, Frank Puppe, and Stefan Schewe. "Graphical knowledge acquisition for medical diagnostic expert systems." Artificial Intelligence in Medicine 5, no. 3 (June 1993): 185–211. http://dx.doi.org/10.1016/0933-3657(93)90024-w.

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22

Birmingham, William, and Georg Klinker. "Knowledge-acquisition tools with explicit problem-solving models." Knowledge Engineering Review 8, no. 1 (March 1993): 5–25. http://dx.doi.org/10.1017/s0269888900000047.

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AbstractIn the past decade, expert systems have been applied to a wide variety of application tasks. A central problem of expert system development and maintenance is the demand placed on knowledge engineers and domain experts. A commonly proposed solution is knowledge-acquisition tools. This paper reviews a class of knowledge-acquisition tools that presuppose the problem-solving method, as well as the structure of the knowledge base. These explicit problem-solving models are exploited by the tools during knowledge-acquisition, knowledge generalization, error checking and code generation.

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23

Adler, Jeffrey L., and Eknauth Persaud. "Knowledge Acquisition for Large-Scale Expert Systems in Transportation." Transportation Research Record: Journal of the Transportation Research Board 1651, no. 1 (January 1998): 59–65. http://dx.doi.org/10.3141/1651-09.

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One of the greatest challenges in building an expert system is obtaining, representing, and programming the knowledge base. As the size and scope of the problem domain increases, knowledge acquisition and knowledge engineering become more challenging. Methods for knowledge acquisition and engineering for large-scale projects are investigated in this paper. The objective is to provide new insights as to how knowledge engineers play a role in defining the scope and purpose of expert systems and how traditional knowledge acquisition and engineering methods might be recast in cases where the exper

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24

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

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25

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 (July 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 k

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26

Fox, J., C. D. Myers, M. F. Greaves, and Susan Pegram. "Knowledge Acquisition for Expert Systems: Experience in Leukaemia Diagnosis." Methods of Information in Medicine 24, no. 02 (April 1985): 65–72. http://dx.doi.org/10.1055/s-0038-1635361.

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SummaryEMYCIN was used to develop an expert system for the interpretation of immunological data obtained in the cell surface phenotyping of leukaemia. Access to a recognised expert, and a large quantity of data on the leukaemias, has facilitated a systematic study of knowledge acquisition and knowledge base refinement based on tape recorded commentaries made by the expert. System performance was analysed at six stages in its development, and ways in which it differed from that of the human diagnostician were identified. Among the most suggestive observations were differences in the way that “u

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27

Fox, J., C. D. Myers, M. F. Greaves, and Susan Pegram. "Knowledge Acquisition for Expert Systems: Experience in Leukaemia Diagnosis." Methods of Information in Medicine 24, no. 02 (April 1985): 65–72. http://dx.doi.org/10.1055/s-0038-1636675.

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SummaryEMYCIN was used to develop an expert system for the interpretation of immunological data obtained in the cell surface phenotyping of leukaemia. Access to a recognised expert, and a large quantity of data on the leukaemias, has facilitated a systematic study of knowledge acquisition and knowledge base refinement based on tape recorded commentaries made by the expert. System performance was analysed at six stages in its development, and ways in which it differed from that of the human diagnostician were identified. Among the most suggestive observations were differences in the way that “u

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28

Forsythe, D. E., and B. G. Buchanan. "Knowledge acquisition for expert systems: some pitfalls and suggestions." IEEE Transactions on Systems, Man, and Cybernetics 19, no. 3 (1989): 435–42. http://dx.doi.org/10.1109/21.31050.

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29

Gale, William A. "A statistical approach to knowledge acquisition for expert systems." Annals of Mathematics and Artificial Intelligence 2, no. 1-4 (March 1990): 149–63. http://dx.doi.org/10.1007/bf01531003.

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30

Vingerhoeds, R. A., P. Delbar, and L. Boullart. "Expert Systems for Process Control Using Automatic Knowledge Acquisition." IFAC Proceedings Volumes 21, no. 13 (September 1988): 55–60. http://dx.doi.org/10.1016/s1474-6670(17)53699-x.

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31

Mars, Nicolaas J. I., and Perry L. Miller. "Knowledge Acquisition and Veritication Tools for Medical Expert Systems." Medical Decision Making 7, no. 1 (February 1987): 6–11. http://dx.doi.org/10.1177/0272989x8700700105.

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32

Yang, Heng-Li. "Information/knowledge acquisition methods for decision support systems and expert systems." Information Processing & Management 31, no. 1 (January 1995): 47–58. http://dx.doi.org/10.1016/0306-4573(95)80005-e.

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33

Molnar, Kathleen K., and Ramesh Sharda. "Using the Internet for Knowledge Acquisition in Expert Systems Development: A Case Study." Journal of Information Technology 11, no. 3 (September 1996): 223–34. http://dx.doi.org/10.1177/026839629601100304.

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Knowledge acquisition is a major task in expert system development. This paper proposes one way of acquiring knowledge for expert system development: through the use of the Internet. Internet resources (e.g. Usenet groups, ListServ discussion lists, archive sites and on-line literature/database searches) are knowledge sources. Internet tools such as newsreaders, electronic mail, Telnet, FTP, gophers, archie, WAIS and World Wide Web provide access to these sources. The results of an exploratory study that examined the use of the Internet as a knowledge source are presented here in conjunction w

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34

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

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35

Feng, Xin, and Thomas A. Weber. "Knowledge Acquisition Advisor (KA2): An interactive knowledge-acquisition tool for expert system development." Engineering Applications of Artificial Intelligence 6, no. 6 (December 1993): 507–18. http://dx.doi.org/10.1016/0952-1976(93)90048-3.

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36

Vasil’ev, O. M., D. P. Vetrov, and D. A. Kropotov. "Knowledge representation and acquisition in expert systems for pattern recognition." Computational Mathematics and Mathematical Physics 47, no. 8 (August 2007): 1373–97. http://dx.doi.org/10.1134/s0965542507080155.

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37

Rafea, A. "Automatic knowledge acquisition tool for irrigation and fertilization expert systems." Expert Systems with Applications 24, no. 1 (January 2003): 49–57. http://dx.doi.org/10.1016/s0957-4174(02)00082-9.

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38

Chuprina, S. I., and A. S. Minin. "Methods and tools to build ontology driven knowledge acquisition systems." Вестник Пермского университета. Математика. Механика. Информатика, no. 4(55) (2021): 25–34. http://dx.doi.org/10.17072/1993-0550-2021-4-25-34.

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The article discusses the main existing methods and tools for automating the process of acquiring knowledge, and also presents the concept of an approach to the application of knowledge engineering methods of building the ontology driven knowledge acquisition systems. Automation of the process of production expert system knowledge base building uses the principles of adaptability to the specifics of the application domain and third-party information resources is demonstrated. A distinctive feature of the proposed approach is the active nature of the dialogue within the knowledge extraction sys

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39

XING, HAO, SAMUEL H. HUANG, and J. SHI. "Rapid development of knowledge-based systems via integrated knowledge acquisition." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 17, no. 3 (August 2003): 221–34. http://dx.doi.org/10.1017/s0890060403173052.

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This paper presents a novel approach, which is based on integrated (automatic/interactive) knowledge acquisition, to rapidly develop knowledge-based systems. Linguistic rules compatible with heuristic expert knowledge are used to construct the knowledge base. A fuzzy inference mechanism is used to query the knowledge base for problem solving. Compared with the traditional interview-based knowledge acquisition, our approach is more flexible and requires a shorter development cycle. The traditional approach requires several rounds of interviews (both structured and unstructured). However, our me

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40

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 (January 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 knowled

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41

de Vries, P. H., and P. F. de Vries Robbé. "An Overview of Medical Expert Systems." Methods of Information in Medicine 24, no. 02 (April 1985): 57–64. http://dx.doi.org/10.1055/s-0038-1635358.

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SummaryExpert systems are an important extension of the research on medical decision making. Their relation to other research in this area is shortly discussed. Fourteen medical expert systems are examined from different perspectives. After a discussion of their goal, domain, and history, the classification of expert systems along the procedural-declarative continuum provides the basis for the analysis of their knowledge representations.As a result of this analysis, types of knowledge are identified that serve as a frame of reference for the comparison of the systems. Subsequently, the central

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42

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 (February 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

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43

Liaqat, Muhammad Sheharyar, Ihtisham ul Haq, Muhammad Burhan, and Shakir Mahmood Mayo. "Knowledge Acquisition System for Sentiment Analysis." Vol 4 Issue 2 4, no. 2 (June 27, 2022): 612–20. http://dx.doi.org/10.33411/ijist/2022040226.

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Human has aptitude to understand the knowledge from different life circumstances, experiences for acquire from these experiences and use itself for the erudition of Common Sense acquaintance for making the imperative conclusions in life. That is principal alteration between Out-dated Artificial Intelligence (AI), Expert Systems (ES) and Humans. Due to this capability, human could directly handle conclusion circumstances but in systems they have deficiency of generality, rules state of mind and Sentiments skills. Human circumstantial experiences communicate about how to live in social cultures

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44

Rybina, G. V., and A. O. Deineko. "Distributed knowledge acquisition for the automatic construction of integrated expert systems." Scientific and Technical Information Processing 38, no. 6 (December 2011): 428–34. http://dx.doi.org/10.3103/s0147688211060062.

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45

Lavrac, Nada, and Igor Mozetic. "Methods for knowledge acquisition and refinement in second generation expert systems." ACM SIGART Bulletin, no. 108 (April 1989): 63–69. http://dx.doi.org/10.1145/63266.63274.

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46

Wagner, W. P., J. Otto, and Q. B. Chung. "Knowledge acquisition for expert systems in accounting and financial problem domains." Knowledge-Based Systems 15, no. 8 (November 2002): 439–47. http://dx.doi.org/10.1016/s0950-7051(02)00026-6.

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47

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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48

Kobayashi, S., and R. Mizoguchi. "Knowledge acquisition and learning for expert systems: Research activities in Japan." IFAC Proceedings Volumes 24, no. 11 (September 1991): 253–58. http://dx.doi.org/10.1016/b978-0-08-041273-3.50049-5.

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49

de Rosa, Francesca, and Alessandro De Gloria. "An Analytical Game for Knowledge Acquisition for Maritime Behavioral Analysis Systems." Applied Sciences 10, no. 2 (January 14, 2020): 591. http://dx.doi.org/10.3390/app10020591.

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The use of Bayesian networks for behavioral analysis is gaining attention. The design of such algorithms often makes use of expert knowledge. The knowledge is collected and organized during the knowledge acquisition design task. In this paper, we discuss how analytical games can be exploited as knowledge acquisition techniques in order to collect information useful to intelligent systems design. More specifically, we introduce a recently developed method, called the MARISA (MARItime Surveillance knowledge Acquisition) Game. The aim of this game is to ease the elicitation from domain experts of

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50

Ren, Yong Chang, Tao Xing, and Ping Zhu. "An Attribute Reduction Algorithms of Expert System Knowledge Acquisition." Applied Mechanics and Materials 48-49 (February 2011): 187–91. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.187.

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Knowledge acquisition is the bottleneck of construction expert system, to provide an accurate inference of knowledge is the key decision-making plan. This article use the rough sets theory, through the rough sets reduction eliminate redundant condition attribute, to achieve the streamlining of the knowledge library. First study the knowledge acquisition, in exposition knowledge hierarchical structure foundation, has given the conceptualization, formal, the knowledge library refinement and so on three knowledge acquisition; and then study attributes reduction algorithms, in the research sets di

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