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Articoli di riviste sul tema "Knowledge acquisition (Expert systems)"

Autore: Grafiati
Pubblicato: 4 giugno 2021
Ultima modifica: 29 luglio 2024

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1

Brodheim, E. "Knowledge acquisition for expert systems". European Journal of Operational Research 32, n. 1 (ottobre 1987): 153. http://dx.doi.org/10.1016/0377-2217(87)90281-5.

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Zimmermann, H. J., e Richard Weber. "Expert systems, automating knowledge acquisition". European Journal of Operational Research 39, n. 3 (aprile 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, n. 2 (gennaio 1988): 208–9. http://dx.doi.org/10.1016/0306-4573(88)90117-3.

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4

Belkin, Brenda L., e Robert F. Stengel. "Quantitative knowledge acquisition for expert systems". Engineering Applications of Artificial Intelligence 3, n. 4 (dicembre 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, n. 6 (1995): 541–60. http://dx.doi.org/10.1002/int.4550100602.

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6

Edwards, Marion, e Roger E. Cooley. "Expertise in expert systems: knowledge acquisition for biological expert systems". Bioinformatics 9, n. 6 (1993): 657–65. http://dx.doi.org/10.1093/bioinformatics/9.6.657.

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7

Hammad, Amin, e Yoshito Itoh. "Knowledge Acquisition for Bridge Design Expert Systems". Computer-Aided Civil and Infrastructure Engineering 8, n. 3 (6 novembre 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, n. 2 (giugno 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 e З. А. Магазиева. "EXPERT SYSTEMS SHELL: AN EFFECTIVE KNOWLEDGE ACQUISITION MODEL". Вестник ГГНТУ. Гуманитарные и социально-экономические науки, n. 2(28) (26 agosto 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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10

Chan, Christine, e Izak Benbasat. "Case research on knowledge acquisition: observations and lessons". Knowledge Engineering Review 6, n. 2 (giugno 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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11

Gaines, Brian. "Knowledge Acquisition Systems For Rapid Prototyping Of Expert Systems". INFOR: Information Systems and Operational Research 26, n. 4 (gennaio 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, n. 3 (settembre 1990): 181. http://dx.doi.org/10.1016/0950-7051(91)90030-6.

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13

Edwards, John S., e Alison L. Kidd. "Knowledge Acquisition for Expert Systems: A Practical Handbook". Journal of the Operational Research Society 40, n. 3 (marzo 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, n. 2 (luglio 1987): 163–73. http://dx.doi.org/10.1177/089443938700500203.

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15

CORDIER, MARIE-ODILE, e CHANTAL REYNAUD. "KNOWLEDGE ACQUISITION TECHNIQUES AND SECOND-GENERATION EXPERT SYSTEMS". Applied Artificial Intelligence 5, n. 3 (gennaio 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, n. 3 (marzo 1989): 303–4. http://dx.doi.org/10.1057/jors.1989.41.

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17

Diederich, Joachim, Ingo Ruhmann e Mark May. "KRITON: a knowledge-acquisition tool for expert systems". International Journal of Man-Machine Studies 26, n. 1 (gennaio 1987): 29–40. http://dx.doi.org/10.1016/s0020-7373(87)80033-0.

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18

Broner, Israel, J. Phillip King e Amnon Nevo. "Structured induction for agricultural expert systems knowledge acquisition". Computers and Electronics in Agriculture 5, n. 2 (ottobre 1990): 87–99. http://dx.doi.org/10.1016/0168-1699(90)90025-k.

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19

Jin-Ming, Zhou, e Xiong Fan-Lun. "Knowledge Acquisition in Fertilization Expert Systems Building Tool". IFAC Proceedings Volumes 22, n. 12 (settembre 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, n. 2 (marzo 1988): 114–20. http://dx.doi.org/10.1016/0950-7051(88)90006-8.

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21

Gappa, Ute, Frank Puppe e Stefan Schewe. "Graphical knowledge acquisition for medical diagnostic expert systems". Artificial Intelligence in Medicine 5, n. 3 (giugno 1993): 185–211. http://dx.doi.org/10.1016/0933-3657(93)90024-w.

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22

Birmingham, William, e Georg Klinker. "Knowledge-acquisition tools with explicit problem-solving models". Knowledge Engineering Review 8, n. 1 (marzo 1993): 5–25. http://dx.doi.org/10.1017/s0269888900000047.

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Abstract (sommario):
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., e Eknauth Persaud. "Knowledge Acquisition for Large-Scale Expert Systems in Transportation". Transportation Research Record: Journal of the Transportation Research Board 1651, n. 1 (gennaio 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 expert system is a component within a larger scale client-server application targeting multiple users.
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24

Lightfoot, Jay M. "Expert knowledge acquisition and the unwilling expert: a knowledge engineering perspective". Expert Systems 16, n. 3 (agosto 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 e L. M. Fagan. "Graphical Access to Medical Expert Systems: III. Design of a Knowledge Acquisition Environment*)". Methods of Information in Medicine 26, n. 03 (luglio 1987): 78–88. http://dx.doi.org/10.1055/s-0038-1635497.

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Abstract (sommario):
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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26

Fox, J., C. D. Myers, M. F. Greaves e Susan Pegram. "Knowledge Acquisition for Expert Systems: Experience in Leukaemia Diagnosis". Methods of Information in Medicine 24, n. 02 (aprile 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 “undiagnosable” patients were treated and a failure of the elicitation technique to reveal structural aspects of the task. The tools and techniques of knowledge engineering are a significant advance, but a better methodology for developing high quality knowledge bases is needed.
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27

Fox, J., C. D. Myers, M. F. Greaves e Susan Pegram. "Knowledge Acquisition for Expert Systems: Experience in Leukaemia Diagnosis". Methods of Information in Medicine 24, n. 02 (aprile 1985): 65–72. http://dx.doi.org/10.1055/s-0038-1636675.

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Abstract (sommario):
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 “undiagnosable” patients were treated and a failure of the elicitation technique to reveal structural aspects of the task. The tools and techniques of knowledge engineering are a significant advance, but a better methodology for developing high quality knowledge bases is needed.
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28

Forsythe, D. E., e B. G. Buchanan. "Knowledge acquisition for expert systems: some pitfalls and suggestions". IEEE Transactions on Systems, Man, and Cybernetics 19, n. 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, n. 1-4 (marzo 1990): 149–63. http://dx.doi.org/10.1007/bf01531003.

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30

Vingerhoeds, R. A., P. Delbar e L. Boullart. "Expert Systems for Process Control Using Automatic Knowledge Acquisition". IFAC Proceedings Volumes 21, n. 13 (settembre 1988): 55–60. http://dx.doi.org/10.1016/s1474-6670(17)53699-x.

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31

Mars, Nicolaas J. I., e Perry L. Miller. "Knowledge Acquisition and Veritication Tools for Medical Expert Systems". Medical Decision Making 7, n. 1 (febbraio 1987): 6–11. http://dx.doi.org/10.1177/0272989x8700700105.

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Yang, Heng-Li. "Information/knowledge acquisition methods for decision support systems and expert systems". Information Processing & Management 31, n. 1 (gennaio 1995): 47–58. http://dx.doi.org/10.1016/0306-4573(95)80005-e.

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33

Molnar, Kathleen K., e Ramesh Sharda. "Using the Internet for Knowledge Acquisition in Expert Systems Development: A Case Study". Journal of Information Technology 11, n. 3 (settembre 1996): 223–34. http://dx.doi.org/10.1177/026839629601100304.

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Abstract (sommario):
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 with a framework for using the Internet in the planning phase. Four major advantages can be found in this: the availability of multiple experts in multiple domains, the interaction of domain experts and end users, time/cost savings, and convenience. The lessons learned and some additional issues are also presented.
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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, n. 2 (giugno 1987): 59–68. http://dx.doi.org/10.1007/bf01206303.

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35

Feng, Xin, e Thomas A. Weber. "Knowledge Acquisition Advisor (KA2): An interactive knowledge-acquisition tool for expert system development". Engineering Applications of Artificial Intelligence 6, n. 6 (dicembre 1993): 507–18. http://dx.doi.org/10.1016/0952-1976(93)90048-3.

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Vasil’ev, O. M., D. P. Vetrov e D. A. Kropotov. "Knowledge representation and acquisition in expert systems for pattern recognition". Computational Mathematics and Mathematical Physics 47, n. 8 (agosto 2007): 1373–97. http://dx.doi.org/10.1134/s0965542507080155.

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Rafea, A. "Automatic knowledge acquisition tool for irrigation and fertilization expert systems". Expert Systems with Applications 24, n. 1 (gennaio 2003): 49–57. http://dx.doi.org/10.1016/s0957-4174(02)00082-9.

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38

Chuprina, S. I., e A. S. Minin. "Methods and tools to build ontology driven knowledge acquisition systems". Вестник Пермского университета. Математика. Механика. Информатика, n. 4(55) (2021): 25–34. http://dx.doi.org/10.17072/1993-0550-2021-4-25-34.

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Abstract (sommario):
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 system (dialogue script is generated in dynamics). This allows, during communication with an expert, to initiate a natural language dialogue adequate to the features of the problem being solved and focus on simplifying and automating the work of an expert and a knowledge engineer. The tools which allow the knowledge engineer to verify the quality of the developed knowledge base are discussed.
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XING, HAO, SAMUEL H. HUANG e J. SHI. "Rapid development of knowledge-based systems via integrated knowledge acquisition". Artificial Intelligence for Engineering Design, Analysis and Manufacturing 17, n. 3 (agosto 2003): 221–34. http://dx.doi.org/10.1017/s0890060403173052.

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Abstract (sommario):
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 method involves an optional initial interview, followed by data collection, automatic rule generation, and an optional final interview/rule verification process. The effectiveness of our approach is demonstrated through a benchmark case study and a real-life manufacturing application.
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Rose, Jacob M., Britton A. McKay, Carolyn Strand Norman e Anna M. Rose. "Designing Decision Aids to Promote the Development of Expertise". Journal of Information Systems 26, n. 1 (1 gennaio 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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de Vries, P. H., e P. F. de Vries Robbé. "An Overview of Medical Expert Systems". Methods of Information in Medicine 24, n. 02 (aprile 1985): 57–64. http://dx.doi.org/10.1055/s-0038-1635358.

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Abstract (sommario):
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 role of these knowledge types in one of the most important tasks of expert systems, explaining, is emphasized. After the knowledge representation, two other components of expert systems are discussed that heavily depend on it: the knowledge acquisition and the man-machine interface. As a conclusion, the status quo of the research on expert systems is outlined and some developments are extrapolated. These developments show a tendency toward the integration of different knowledge types in one system.
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42

RICHTER, HEATHER, GREGORY ABOWD, CHRIS MILLER e HARRY FUNK. "TAGGING KNOWLEDGE ACQUISITION SESSIONS TO FACILITATE KNOWLEDGE TRACEABILITY". International Journal of Software Engineering and Knowledge Engineering 14, n. 01 (febbraio 2004): 3–19. http://dx.doi.org/10.1142/s0218194004001543.

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Abstract (sommario):
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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Liaqat, Muhammad Sheharyar, Ihtisham ul Haq, Muhammad Burhan e Shakir Mahmood Mayo. "Knowledge Acquisition System for Sentiment Analysis". Vol 4 Issue 2 4, n. 2 (27 giugno 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 where the common-sense knowledge is very vigorous. Subsequently Opinion Mining become very substantial method in now days. Two core categories for the system how to grow Sentimental Knowledge Acquisitions with the help of these two. Analyses around diverse methods used for Opinion Mining and forthcoming work might be ensue in approaching years for Sentimental Knowledge Acquisition.
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44

Rybina, G. V., e A. O. Deineko. "Distributed knowledge acquisition for the automatic construction of integrated expert systems". Scientific and Technical Information Processing 38, n. 6 (dicembre 2011): 428–34. http://dx.doi.org/10.3103/s0147688211060062.

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45

Lavrac, Nada, e Igor Mozetic. "Methods for knowledge acquisition and refinement in second generation expert systems". ACM SIGART Bulletin, n. 108 (aprile 1989): 63–69. http://dx.doi.org/10.1145/63266.63274.

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46

Wagner, W. P., J. Otto e Q. B. Chung. "Knowledge acquisition for expert systems in accounting and financial problem domains". Knowledge-Based Systems 15, n. 8 (novembre 2002): 439–47. http://dx.doi.org/10.1016/s0950-7051(02)00026-6.

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47

POULYMENAKOU, A., T. CORNFORD e E. WHITLEY. "Knowledge acquisition for organisational problem solving: Developing expert systems and beyond☆". Expert Systems with Applications 5, n. 1-2 (1992): 121–30. http://dx.doi.org/10.1016/0957-4174(92)90101-w.

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48

Kobayashi, S., e R. Mizoguchi. "Knowledge acquisition and learning for expert systems: Research activities in Japan". IFAC Proceedings Volumes 24, n. 11 (settembre 1991): 253–58. http://dx.doi.org/10.1016/b978-0-08-041273-3.50049-5.

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49

de Rosa, Francesca, e Alessandro De Gloria. "An Analytical Game for Knowledge Acquisition for Maritime Behavioral Analysis Systems". Applied Sciences 10, n. 2 (14 gennaio 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 a considerable amount of conditional probabilities to be encoded into a maritime behavioral analysis service based on a multi-source dynamic Bayesian network. The game has been deployed in two experiments. The main objectives of such experiments are the validation of the network structure, the acquisition of the conditional probabilities for the network, and the overall validation of the game method. The results of the experiment show that the objectives have been met and that the MARISA Game proved to be an effective and efficient approach.
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Ren, Yong Chang, Tao Xing e Ping Zhu. "An Attribute Reduction Algorithms of Expert System Knowledge Acquisition". Applied Mechanics and Materials 48-49 (febbraio 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 difference and the attribute importance, the reduction algorithms inferential reasoning process's foundation, has given the attribute reduction algorithms six steps. Finally, according to the attributes reduction algorithms and the steps, to estimate the expert system to the function analytic method construction software cost, the composition technology complexity factor of 14 factors reduction. The results showed that the use of rough sets theory to reduce the attributes, can simplify the structure of complex systems, and can effectively maintain the knowledge library structure and performance.
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