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Relevant bibliographies by topics / Knowledge acquisition (Expert systems)

Academic literature on the topic 'Knowledge acquisition (Expert systems)'

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Published: 4 June 2021

Last updated: 29 July 2024

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

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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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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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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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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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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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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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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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 techniques. A survey of the literature indicates increasing emphasis on tools for knowledge acquisition, used directly by experts. Several projects currently directed towards providing a proper epistemological foundation for knowledge acquisition are discussed and compared. None has yet produced a complete epistemologically sound methodology; however, recognition of the need to create a conceptual model at the knowledge level (rather than the symbol level) is an important advance. An extensive bibliography is appended.

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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 применения. Причины этого могут быть обусловлены сущностью технических факторов, финансовых последствий, культурных и религиозных убеждений и ограничений, связанных с внедрением экспертных систем, особенно в слаборазвитых странах, либо отсутствием достаточных знаний о существовании оболочек экспертных систем и предоставляемых ими средств, утилит и инструментов, которые делают разработку новых экспертных систем своего рода «довольно легким делом». В этой статье основное внимание уделяется базовой концепции оболочек экспертных систем с целью выделения их базового использования и создания некоторой формы углубленного общего представления о некоторых компонентах и утилит, которые они предоставляют, предлагая более эффективную модель получения знаний, которая позволила бы устранить узкое место в приобретении знаний при разработке экспертных систем, а также выделить функции, выполняемые оболочками экспертных систем, в попытке подчеркнуть их возможность повторного использования в различных других конкретных областях применения внутри, либо за пределами одной и той же области. An expert system is an intelligent computer program that uses knowledge and inference procedures to solve problems that are considered complex enough to have enough human experience to solve them. Since the introduction of the first expert systems in the early 1970s and the subsequent successes combined with the benefits that humanity received from these early expert systems and their successors. one would expect that five decades later there would be a boom in the use of expert systems to perform more specialized tasks in specific applications. The reasons for this may be due to the nature of technical factors, financial consequences, cultural and religious beliefs and restrictions associated with the introduction of expert systems, especially in underdeveloped countries, or the lack of sufficient knowledge about the existence of expert system shells and the tools, utilities and tools provided by them that make the development of new expert systems a kind of “pretty easy thing”. This article focuses on the basic concept of expert system shells in order to highlight their basic use and create some form of in-depth general understanding of some of the components and utilities they provide, offering a more effective knowledge acquisition model that would eliminate the bottleneck in knowledge acquisition in the development of expert systems, as well as highlight the functions performed by expert system shells, in an attempt to emphasize their reusability in various other specific applications within, or outside the same area.

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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 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.

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Dissertations / Theses on the topic "Knowledge acquisition (Expert systems)"

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Trimble, John. "Knowledge acquisition and the system dynamics methodology." Diss., Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/23337.

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Parsons, John Scott. "Automated knowledge acquisition for knowledge-based systems, KE-RIT : the Use of Kelleys' personal construct theory in the automation of knowledge acquisitions (theory and prototype) /." Online version of thesis, 1992. http://hdl.handle.net/1850/11056.

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Buck, Arlene J. "Automated knowledge acquisition tool for identification of generic tasks /." Online version of thesis, 1990. http://hdl.handle.net/1850/10577.

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Thorpe, Patrick J. "Knowledge acquisition for expert systems in fibre production." Thesis, Loughborough University, 1992. https://dspace.lboro.ac.uk/2134/13749.

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The aim of the study described in this thesis is to investigate the application of expert system technology to acrylic fibre production, with a particular emphasis on knowledge acquisition requirements. In doing so, it is intended to provide an understanding of the requirements and appropriate techniques for the effective application of expert systems in the process industries; The scope of the study is limited to process fault administration which involves detecting, diagnosing arid correcting abnormalities in process operation. A methodology is provided for the systematic development of expert systems within the defined area of application. An important phase in the development methodology is that of expert system specification. This involves an analysis of expert behaviour and the specification of expert system functionality: In order to assist system specification, generic knowledge types and human expert activities have been identified within the context of process fault administration. Knowledge acquisition is discussed in terms of the requirements during each phase of the proposed development methodology. A detailed review is given of the available techniques for knowledge acquisition and an assessment is presented of the most appropriate techniques to apply during each phase of the methodology. A new knowledge acquisition technique is described. The technique is designed to record knowledge of process operation and process fault diagnosis. It is based on a hierarchical decomposition of the process in terms of process objectives. Two complementary forms of knowledge representation are produced: a hierarchy diagram which shows the dependency relationships between individual process objectives and a task statement table which provides a more. detailed explanation of the objectives. Finally, three. case studies are described in which the techniques described in the thesis were applied and developed.

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Mitchell, F. "Painless knowledge acquisition for time series data." Thesis, University of Aberdeen, 1997. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU100889.

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Knowledge Acquisition has long been acknowledged as the bottleneck in producing Expert Systems. This is because, until relatively recently, the KA (Knowledge Acquisition) process has concentrated on extracting knowledge from a domain expert, which is a very time consuming process. Support tools have been constructed to help this process, but these have not been able to reduce the time radically. However, in many domains, the expert is not the only source of knowledge, nor indeed the best source of knowledge. This is particularly true in industrial settings where performance information is routinely archived. This information, if processed correctly, can provide a substantial part of the knowledge required to build a KB (Knowledge Base). In this thesis I discuss current KA approaches and then go on to outline a methodology which uses KD (Knowledge Discovery) techniques to mine archived time series data to produce fault detection and diagnosis KBs with minimal expert input. This methodology is implemented in the TIGON system, which is the focus of this thesis. TIGON uses archived information (in TIGON's case the information is from a gas turbine engine) along with guidance from the expert to produce KBs for detecting and diagnosing faults in a gas turbine engine. TIGON's performance is also analysed in some detail. A comparison with other related work is also included.

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Youn, Bong-Soo. "Intelligent knowledge acquisition system /." Online version of thesis, 1989. http://hdl.handle.net/1850/10444.

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Curtis, Gary James. "Object-oriented design for knowledge acquisition in expert systems." Thesis, University of Southampton, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.293353.

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Motta, Enrico. "Reusable components for knowledge modelling." N.p, 1997. http://ethos.bl.uk/.

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Corsar, David. "Developing knowledge-based systems through ontology mapping and ontology guided knowledge acquisition." Thesis, Available from the University of Aberdeen Library and Historic Collections Digital Resources, 2009. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?application=DIGITOOL-3&owner=resourcediscovery&custom_att_2=simple_viewer&pid=25800.

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Suryanto, Hendra Computer Science &amp Engineering Faculty of Engineering UNSW. "Learning and discovery in incremental knowledge acquisition." Awarded by:University of New South Wales. School of Computer Science and Engineering, 2005. http://handle.unsw.edu.au/1959.4/20744.

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Knowledge Based Systems (KBS) have been actively investigated since the early period of AI. There are four common methods of building expert systems: modeling approaches, programming approaches, case-based approaches and machine-learning approaches. One particular technique is Ripple Down Rules (RDR) which may be classified as an incremental case-based approach. Knowledge needs to be acquired from experts in the context of individual cases viewed by them. In the RDR framework, the expert adds a new rule based on the context of an individual case. This task is simple and only affects the expert???s workflow minimally. The rule added fixes an incorrect interpretation made by the KBS but with minimal impact on the KBS's previous correct performance. This provides incremental improvement. Despite these strengths of RDR, there are some limitations including rule redundancy, lack of intermediate features and lack of models. This thesis addresses these RDR limitations by applying automatic learning algorithms to reorganize the knowledge base, to learn intermediate features and possibly to discover domain models. The redundancy problem occurs because rules created in particular contexts which should have more general application. We address this limitation by reorganizing the knowledge base and removing redundant rules. Removal of redundant rules should also reduce the number of future knowledge acquisition sessions. Intermediate features improve modularity, because the expert can deal with features in groups rather than individually. In addition to the manual creation of intermediate features for RDR, we propose the automated discovery of intermediate features to speed up the knowledge acquisition process by generalizing existing rules. Finally, the Ripple Down Rules approach facilitates rapid knowledge acquisition as it can be initialized with a minimal ontology. Despite minimal modeling, we propose that a more developed knowledge model can be extracted from an existing RDR KBS. This may be useful in using RDR KBS for other applications. The most useful of these three developments was the automated discovery of intermediate features. This made a significant difference to the number of knowledge acquisition sessions required.

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Books on the topic "Knowledge acquisition (Expert systems)"

1

Brulé, James F. Knowledge acquisition. New York: McGraw-Hill, 1989.

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Hart, Anna. Knowledge acquisition for expert systems. 2nd ed. New York: McGraw-Hill, 1992.

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1943-, Dillon Tharam S., and Knowledge Systems Research Pty Ltd., eds. Automated knowledge acquisition. New York: Prentice Hall, 1994.

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Kidd, Alison L., ed. Knowledge Acquisition for Expert Systems. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-1823-1.

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Wu, Xindong. Knowledge acquisition from databases. Norwood, N.J: Ablex Pub., 1995.

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McGraw, Karen L. Knowledge acquisition: Principles and guidelines. Englewood Cliffs, N.J: Prentice-Hall, 1989.

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Muggleton, S. Inductive acquisition of expert knowledge. Wokingham: Addison-Wesley/Turing Institute Press, 1989.

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Marcus, Sandra, ed. Automating Knowledge Acquisition for Expert Systems. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4684-7122-9.

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Marcus, Sandra. Automating Knowledge Acquisition for Expert Systems. Boston, MA: Springer US, 1988.

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Fensel, Dieter. The knowledge acquisition and representation language, KARL. Boston: Kluwer Academic Publishers, 1995.

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Book chapters on the topic "Knowledge acquisition (Expert systems)"

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Mohan, Chilukuri Krishna. "Knowledge Acquisition." In Frontiers of Expert Systems, 201–36. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4509-5_8.

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Grzymala-Busse, Jerzy W. "Knowledge Acquisition." In Managing Uncertainty in Expert Systems, 43–76. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-3982-7_3.

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Kidd, Alison L. "Knowledge Acquisition." In Knowledge Acquisition for Expert Systems, 1–16. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-1823-1_1.

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Larichev, O. I., A. I. Mechitov, E. M. Moshkovich, and E. M. Furems. "Expert Knowledge Acquisition System." In Lecture Notes in Economics and Mathematical Systems, 517–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-49298-3_48.

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van Dijk, J. C., and Paul Williams. "Knowledge, Representation and Acquisition." In Expert Systems in Auditing, 36–49. London: Palgrave Macmillan UK, 1990. http://dx.doi.org/10.1007/978-1-349-12474-9_5.

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Wielinga, B. J., B. Bredeweg, and J. A. Breuker. "Knowledge acquisition for expert systems." In Advanced Topics in Artificial Intelligence, 96–124. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/3-540-50676-4_11.

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Hart, Anna. "Knowledge Acquisition for Expert Systems." In Knowledge, Skill and Artificial Intelligence, 103–11. London: Springer London, 1988. http://dx.doi.org/10.1007/978-1-4471-1632-5_10.

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Musen, Mark A. "An Overview of Knowledge Acquisition." In Second Generation Expert Systems, 405–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-77927-5_18.

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Johnson, Leslie, and Nancy E. Johnson. "Knowledge Elicitation Involving Teachback Interviewing." In Knowledge Acquisition for Expert Systems, 91–108. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-1823-1_5.

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Hart, Anna. "Role of Induction in Knowledge Elicitation." In Knowledge Acquisition for Expert Systems, 165–89. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-1823-1_8.

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Conference papers on the topic "Knowledge acquisition (Expert systems)"

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Becker, Lee A., and Luke Immes. "An approach to automating knowledge acquisition for expert systems." In the 15th annual conference. New York, New York, USA: ACM Press, 1987. http://dx.doi.org/10.1145/322917.322938.

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Madhusudanan, N., and Amaresh Chakrabarti. "Combining Product Information and Process Information to Build Virtual Assembly Situations for Knowledge Acquisition." In ASME 2011 World Conference on Innovative Virtual Reality. ASMEDC, 2011. http://dx.doi.org/10.1115/winvr2011-5518.

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Assembly is an important part of the product development process. To avoid potential issues during assembly in specialized domains such as aircraft assembly, expert knowledge to predict such issues is helpful. Knowledge based systems can act as virtual experts to provide assistance. Knowledge acquisition for such systems however, is a challenge, and this paper describes one part of an ongoing research to acquire knowledge through a dialog between an expert and a knowledge acquisition system. In particular this paper discusses the use of a situation model for assemblies to present experts with a virtual assembly and help them locate the specific context of the knowledge they provide to the system.

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M.AHMED, IBRAHIM, MARCO ALFONSE, ABEER M.MAHMOUD, and ABDEL-BADEEH M.SALEM. "Knowledge Acquisition for Developing Knowledge-Base of Diabetic Expert System." In The 7th International Conference on Information Technology. Al-Zaytoonah University of Jordan, 2015. http://dx.doi.org/10.15849/icit.2015.0004.

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Aguirre, Jose-Luis, Olivia Barron, Ramon Brena, and Maria-Nelly Garcia. "Using a high level knowledge representation for expert systems knowledge acquisition and prototyping." In the 1993 ACM/SIGAPP symposium. New York, New York, USA: ACM Press, 1993. http://dx.doi.org/10.1145/162754.165210.

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Jiang, Lianxiang, Huawang Li, Genqing Yang, and Qinrong Yang. "Knowledge Acquisition Model for Satellite Fault Diagnosis Expert System." In 2009 International Conference on Computational Intelligence and Software Engineering. IEEE, 2009. http://dx.doi.org/10.1109/cise.2009.5366271.

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Belkin, B. L., and R. F. Stengel. "Systematic methods for knowledge acquisition and expert system development." In 29th IEEE Conference on Decision and Control. IEEE, 1990. http://dx.doi.org/10.1109/cdc.1990.204015.

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Sundararajan, Srikanth. "Flexible scheme for knowledge acquisition in expert system development." In Orlando '90, 16-20 April, edited by Mohan M. Trivedi. SPIE, 1990. http://dx.doi.org/10.1117/12.21067.

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Cheetham, William E., and Bruce Pomeroy. "Knowledge acquisition for a fluid bed reactor expert system." In SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, edited by Wray Buntine and Doug H. Fisher. SPIE, 1994. http://dx.doi.org/10.1117/12.169394.

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Annaiahshetty, K., and N. Prasad. "Expert System for Multiple Domain Experts Knowledge Acquisition in Software Design and Development." In 2013 UKSim 15th International Conference on Computer Modelling and Simulation (UKSim 2013). IEEE, 2013. http://dx.doi.org/10.1109/uksim.2013.124.

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Neshat, M., M. Yaghobi, M. B. Naghibi, and A. Esmaelzadeh. "Fuzzy Expert System Design for Diagnosis of Liver Disorders." In 2008 International Symposium on Knowledge Acquisition and Modeling (KAM). IEEE, 2008. http://dx.doi.org/10.1109/kam.2008.43.

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Reports on the topic "Knowledge acquisition (Expert systems)"

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Trimble, E. G., R. J. Allwood, and A. E. Bryman. Knowledge Acquisition for Expert Systems in Construction. Fort Belvoir, VA: Defense Technical Information Center, December 1985. http://dx.doi.org/10.21236/ada192403.

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Trimble, E. G., R. J. Allwood, and A. E. Bryman. Knowledge Acquisition for Expert Systems in Construction. Fort Belvoir, VA: Defense Technical Information Center, March 1986. http://dx.doi.org/10.21236/ada192465.

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Trimble, E. G., R. J. Allwood, and A. E. Bryman. Knowledge Acquisition for Expert Systems in Construction. Fort Belvoir, VA: Defense Technical Information Center, August 1986. http://dx.doi.org/10.21236/ada192466.

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Trimble, E. G., R. J. Allwood, and A. E. Bryman. Knowledge Acquisition for Expert Systems in Construction. Fort Belvoir, VA: Defense Technical Information Center, January 1987. http://dx.doi.org/10.21236/ada192467.

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Trimble, E. G., R. J. Allwood, A. E. Bryman, and C. N. Cooper. Knowledge Acquisition for Expert Systems in Construction. Fort Belvoir, VA: Defense Technical Information Center, February 1988. http://dx.doi.org/10.21236/ada192468.

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Trimble, E. G., R. J. Allwood, and A. E. Bryman. Knowledge Acquisition for Expert Systems in Construction. Fort Belvoir, VA: Defense Technical Information Center, August 1987. http://dx.doi.org/10.21236/ada192490.

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Heatherton, James R., and Todd T. Vikan. An Introduction to Expert Systems and Knowledge Acquisition Techniques. Fort Belvoir, VA: Defense Technical Information Center, September 1990. http://dx.doi.org/10.21236/ada227799.

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Geiwitz, James, Robert L. Klatzky, and Brian P. McCloskey. Knowledge Acquisition Techniques for Expert Systems: Conceptual and Empirical Comparisons. Fort Belvoir, VA: Defense Technical Information Center, May 1988. http://dx.doi.org/10.21236/ada219851.

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Kim, Simon S., Mary Lou Maher, Raymond E. Levitt, Martin F. Rooney, and Thomas J. Siller. Survey of the State-of-the-Art Expert/Knowledge Based Systems in Civil Engineering. Fort Belvoir, VA: Defense Technical Information Center, October 1986. http://dx.doi.org/10.21236/ada175257.

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Cao, Larry. IV. Chatbot, Knowledge Graphs, and AI Infrastructure. CFA Institute Research Foundation, April 2023. http://dx.doi.org/10.56227/23.1.10.

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Expert contributors discuss AI and big data applications that are being developed for financial services, such as AI-powered intelligent customer service systems; “factories” for data processing, AI, simulation, and visualization; and symbolic AI.

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