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Journal articles on the topic 'Use of expert systems'

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Published: 28 May 2022
Last updated: 29 May 2022

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1

Alty, J. L. "Use of expert systems." Computer-Aided Engineering Journal 2, no. 1 (1985): 2. http://dx.doi.org/10.1049/cae.1985.0001.

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2

BORMAN, STU. "Use of environmental expert systems growing." Chemical & Engineering News 67, no. 41 (1989): 25a—27. http://dx.doi.org/10.1021/cen-v067n041.p025a.

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3

Kershner, J. D., and R. D. Lorenz. "Construction Techniques for Factory Floor Expert Systems." Journal of Engineering for Industry 110, no. 4 (1988): 395–97. http://dx.doi.org/10.1115/1.3187899.

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This paper addresses the specific problems encountered when building an expert system for use in a manufacturing process. These problems include: general unfamiliarity of the experts with expert systems, the diffused nature of the expertise, the low availability of the experts, and the users fear of being replaced or constrained by the computer system. The paper addresses these problems by presenting specific strategies developed in the construction of an expert system for use in controlling a plating process for electronic components.
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4

Nolte, Alexander, and Michael Prilla. "Anyone can use Models." International Journal of e-Collaboration 9, no. 4 (2013): 45–60. http://dx.doi.org/10.4018/ijec.2013100104.

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Models play an important role in modern organizations. They are used to coordinate the interplay of stakeholders, inform the design of software systems and are even used for controlling purposes. While these models affect multiple people within an organization their creation and usage is limited to a few experts. This is due to the common belief that non-expert modelers are not capable of performing modeling tasks or working with models without the help of experts. With this paper the authors argue that people are capable of interacting with models when they are given the right means to do so. The authors shed light onto the potential benefits of non-expert model interaction by conducting multiple case studies and describing suitable tool support for non-expert modelers.
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5

Beránková, M., L. Dömeová, and M. Houška. "User-oriented methodology of communication with expert systems." Agricultural Economics (Zemědělská ekonomika) 54, No. 5 (2008): 193–201. http://dx.doi.org/10.17221/248-agricecon.

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The article deals with communication between a user and an expert system, especially from the viewpoint of psychology aspects of the communication process. Psychological factor of the communication between the user and the expert system seems to be marginal, although it is a necessary part of a final methodology of the expert system development. Domain expert, knowledge engineer as well as IT expert, who takes part in the expert system creation, usually sets up a unified and static communication interface that is identical for all users. The communication process form highly influences user’s satisfaction of working with the expert system and his willingness to reuse that system. User’s satisfaction expressed by some metrics is one of the key indicators of both efficiency and effectiveness of the expert system. If the system is not used because of the user’s discontent, the money spent for its creation cannot be understood as an investment (asset), but as an expense. In the article, there are classified the users from the point of view of their personality type and some recommendations for a communication project development in the frame of the methodology of the expert system creation are suggested.
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Wrzalik, Artur, and Borut Jereb. "Use of Expert Systems in Crisis Management." System Safety: Human - Technical Facility - Environment 1, no. 1 (2019): 406–11. http://dx.doi.org/10.2478/czoto-2019-0052.

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AbstractThe today’s change in climatic conditions results in weather phenomena occurring increasingly more frequent in Poland that can lead to crisis situations. Also the growing number of high or increased risk of serious industrial failure establishments causes greater probability of adverse and harmful effects for the environment and the population. A response to the need for minimization of those and other threats is effective crisis management. This management should be supported by IT solutions. The paper proposes to use a selected artificial intelligent method - expert systems - in the crisis management process. An expert system is a computer program adopting the knowledge and reasoning mechanisms to solve problems that require experience and expertise. The idea of expert systems is to transfer the knowledge held by a domain expert into a knowledge base, which is applied by the reasoning mechanism. The article presents some results from surveys carried out in selected local government entities of the Silesian voivodeship as regards the interest in implementation of artificial intelligence in the discussed field, and proposes an expert system for a particular crisis situation.
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7

Cass, Kimberly. "Expert Systems as General-Use Advisory Tools." Business and Professional Ethics Journal 15, no. 4 (1996): 61–85. http://dx.doi.org/10.5840/bpej199615412.

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8

Tamer Cavusgil, S., and Cuneyt Evirgen. "Use of expert systems in international marketing." European Journal of Marketing 31, no. 1 (1997): 73–86. http://dx.doi.org/10.1108/03090569710157043.

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9

Hodgson, R., and H. Oman. "Battery data for use in expert systems." Journal of Power Sources 17, no. 1-3 (1986): 171–80. http://dx.doi.org/10.1016/0378-7753(86)80031-3.

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10

Birts, A., L. Mcaulay, and M. King. "Financial Analysis and Expert Systems." Journal of Information Technology 8, no. 3 (1993): 184–94. http://dx.doi.org/10.1177/026839629300800308.

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Financial analysis expert systems have been developed but there is little evidence to suggest that such expert systems are as widely used in the financial analyst community as other information technology products. The research findings presented here suggest that one explanation for this non-use of expert systems may be that users do not have a need to use the existing technology. We arrived at this conclusion by researching the rich picture of the task and the needs of a small number of financial analysts. In so doing, we were able to develop insights into the topic area which to our knowledge are new. Our analysis may confirm a need to re-orientate expert systems development. Thus, the selection and use of research methods which are geared to shedding light on users’ needs may be valuable to practitioners, in developing product development strategies, and to researchers, in casting new light on their topic areas.
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11

Sapegin, Sergey, Gennady Riabtsev, Elena Zubareva, and Igor Basantsov. "TOOL FOR EFFECTIVE EXPERT INTEROPERABILITY IN E-GOVERNMENT DECISION SUPPORT SYSTEMS." Journal of Automation and Information sciences 2 (March 1, 2021): 38–50. http://dx.doi.org/10.34229/1028-0979-2021-2-4.

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The article solves the problem of low efficiency of traditional methods for organizing the expert interaction in e-government decision support systems. It is shown that the activity of a highly qualified expert is still insufficiently organized, systematized and informationally provided, although expert assessments significantly affect the effectiveness, efficiency and economy of decisions. The requirements for automated expert systems are formulated. The features of the interaction of experts with each other and with the decision maker are taken into account. The place of automated expert systems in e-government decision support systems has been identified. It is shown that qualified users (experts who are able to evaluate (transform) the data, to generate variants of decision, to establish the rules for choosing the best) are an integral "element" of decision support systems architecture. At the same time, automated expert systems are mandatory component of decision support systems. An expert interoperability software tool, the PsycheaEXPERTUS automated system, is developed and implemented. Testing of this system confirmed that the use of such systems in e-government will simplify expert procedures, reduce unproductive time spent on organizing and conducting face-to-face consultations, and increase the efficiency of attracting specialists to make public policy, when the uncertainty of the environment and resource constraints exist. Further research is planned to be aimed at overcoming the psychological unpreparedness of decision makers for the widespread use of expert systems in Ukraine.
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12

Cesa-Bianchi, Nicolò, Yoav Freund, David Haussler, David P. Helmbold, Robert E. Schapire, and Manfred K. Warmuth. "How to use expert advice." Journal of the ACM 44, no. 3 (1997): 427–85. http://dx.doi.org/10.1145/258128.258179.

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13

Kaynak, E., and K. Venkataraman. "Expert Systems in International Marketing." Journal of Information Technology 7, no. 4 (1992): 245–59. http://dx.doi.org/10.1177/026839629200700407.

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This article examines the use of expert systems in international marketing. In view of the increasing importance of internationalization of business around the world, a new generation of computer information systems known as Expert Systems offer a unique potential in international market entry and constantly changing global market conditions. The specific purposes of this paper are to develop a set of evaluative criteria for the use of expert systems, examine the nature of decisions in major international marketing decision areas, and evaluate each decision area against the set of criteria to determine if expert systems are suitable for that decision area.
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14

Crassweller, R. M., J. W. Travis, E. G. Rajotte, J. McClure, and P. H. Heinemann. "BUILDING EXPERT SYSTEMS FOR USE BY COMMERCIAL FARMERS." Acta Horticulturae, no. 313 (October 1992): 205–8. http://dx.doi.org/10.17660/actahortic.1992.313.24.

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15

Mandrikova, Ludmila, Veronika Posternakova, Inessa Krasovska, and Tetyana Symovych. "THE ANALYSIS OF MEDICAL EXPERT SYSTEMS’ USE RISKS." Advanced Information Systems 3, no. 2 (2019): 5–9. http://dx.doi.org/10.20998/2522-9052.2019.2.01.

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16

Likothanassis, S. D., P. Adamidis, and C. Giogios. "Use of neural networks in medical expert systems." Medical Informatics 20, no. 4 (1995): 349–57. http://dx.doi.org/10.3109/14639239509024288.

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17

CASSANO, ANTHONY J. "Use of Expert Systems to Enhance Logistic Management." Naval Engineers Journal 102, no. 3 (1990): 119–26. http://dx.doi.org/10.1111/j.1559-3584.1990.tb02637.x.

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18

Krcmar, H. A. O. "Criteria to Select Expert Systems for Business Use." IFAC Proceedings Volumes 20, no. 10 (1987): 159–60. http://dx.doi.org/10.1016/s1474-6670(17)59052-7.

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19

Spahr, S. L., L. R. Jones, and D. E. Dill. "Expert Systems—Their Use in Dairy Herd Management." Journal of Dairy Science 71, no. 3 (1988): 879–85. http://dx.doi.org/10.3168/jds.s0022-0302(88)79631-9.

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20

Li, Zitong, and Mikko J. Sillanpää. "Efficient use of systems mapping without expert knowledge." Physics of Life Reviews 13 (June 2015): 192–93. http://dx.doi.org/10.1016/j.plrev.2015.04.001.

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21

SPELLER, GORDON J., and JOHN A. BRANDON. "Ethical dilemmas constraining the use of Expert Systems." Behaviour & Information Technology 5, no. 2 (1986): 141–43. http://dx.doi.org/10.1080/01449298608914506.

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22

Maggs, Peter B. "The Use of Expert Systems in Comparative Law." American Journal of Comparative Law 42, suppl_2 (1994): 801–12. http://dx.doi.org/10.1093/ajcl/42.suppl2.801.

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23

Coakes, Elayne, Kim Merchant, and Brian Lehaney. "The use of expert systems in business transformation." Management Decision 35, no. 1 (1997): 53–57. http://dx.doi.org/10.1108/00251749710160197.

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24

York, Jeremy. "Use of the Gibbs sampler in expert systems." Artificial Intelligence 56, no. 2-3 (1992): 397–98. http://dx.doi.org/10.1016/0004-3702(92)90032-s.

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York, Jeremy. "Use of the Gibbs sampler in expert systems." Artificial Intelligence 56, no. 1 (1992): 115–30. http://dx.doi.org/10.1016/0004-3702(92)90066-7.

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26

WITLOX, F. "Expert systems in land-use planning: An overview." Expert Systems with Applications 29, no. 2 (2005): 437–45. http://dx.doi.org/10.1016/j.eswa.2005.04.041.

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27

Erdmann, H. H., H. D. Engelmann, W. Burgard, and K. H. Simmrock. "The use of expert systems in chemical engineering." Chemical Engineering and Processing: Process Intensification 23, no. 2 (1988): 125–33. http://dx.doi.org/10.1016/0255-2701(88)80006-0.

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28

Alshare, Khaled A., Mohammad Kamel Alomari, Peggy L. Lane, and Ronald D. Freeze. "Development and determinants of end-user intention: usage of expert systems." Journal of Systems and Information Technology 21, no. 2 (2019): 166–85. http://dx.doi.org/10.1108/jsit-08-2018-0108.

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Purpose Expert systems (ES) design emulates expertise with the intention that the ES be used by non-experts. This study aims to predict end-user intention and use of ES by proposing a research model that extends the basic components of the unified theory of acceptance and use of technology (UTAUT) by including additional relevant factors to ES, including the expert domain, perceived relevance, reliability, quality of ES and management support, which directly and indirectly influence the end-user intention to use an expert system. Design/methodology/approach A structural equation model (SEM), using LISREL, was used to test the measurement and structural models using a sample of 205 end-users of expert systems in the USA. These users of expert systems come from a variety of domains. The factors include both internal and external factors for the individual level of analysis design of this study. Findings The results showed behavioral intention had the strongest effect on usage, followed by perceived relevance. With respect to the factors that impact intention, perceived relevance had the strongest total effect, followed by attitude. For attitude, effort expectancy had the strongest total effect, followed by management support and perceived relevance. Research limitations/implications The results of this study should assist decision-makers in planning training and communications about the use of expert systems so that the expert systems will be used as intended. Originality/value The originality of this work resides in the addition of external factors to the UTAUT model that helps provide advice to practitioners in the support needed to insure expert system implementation success.
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29

Akers, Michael D., Robert E. Jordan, and Grover L. Porter. "An Examination Of Management Accountants Use And Perception Of Expert Systems." Review of Business Information Systems (RBIS) 3, no. 2 (1999): 59–63. http://dx.doi.org/10.19030/rbis.v3i2.5428.

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Although there has been a number of articles written about the possible use of expert systems by management accountants, there has been limited research to support such predictions. The primary purposes of this paper are to examine management accoun-tants use and perceptions of expert systems. A survey instrument was designed to eli-cit responses (Likert scale and open-ended) about management accountants' perceptions of the current and future use of expert systems and to determine their knowledge, interest and involvement with such systems. The findings of this study indicate that although respondents are moderately interested and involved with expert systems, they have a limited knowledge of those systems. The respondents do agree that expert systems will be utilized more in the future, and that management accountants will be involved in the development and use of expert systems. Expert systems will be utilized to reduce inventories and improve productivity. The respondents, however, do not agree that expert systems are currently used for the management accounting applications used in this study. Also, their expectations regarding the current and future use of expert systems are significantly different. Expectations of the future use of expert system can be found in management accounting literature over the past fifteen years. The findings of this study indicate prior expectations have not materialized.
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30

McClure, Janice E. "DEVELOPMENT OF AN EXPERT SYSTEM." HortScience 26, no. 6 (1991): 795B—795. http://dx.doi.org/10.21273/hortsci.26.6.795b.

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The development of expert systems in agriculture consists of many steps such as problem definition, selection of experts, audience considerations, knowledge representation, coding, testing, and feedback. The problem definition and selection of experts for the problem domain are the foundation of a working system. Audience definition, economics and goal setting are areas that must be documented before knowledge engineering. Knowledge representation methods and system conceptual layout are the next level of development. The use of the user feedback and field testing data to improve the system are often overlooked. Benefits of expert systems for on farm decision making include education, efficiency, and adaption to changing regulations. Many aspects of agricultural expert systems are similar to traditional expert systems; yet special problem inherent in agriculture make the development interesting and challenging.
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Kumar, Sandeep, and Ravi Bhushan Mishra. "Web-based expert systems and services." Knowledge Engineering Review 25, no. 2 (2010): 167–98. http://dx.doi.org/10.1017/s0269888910000020.

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AbstractWeb-based expert systems (WBESs) provide the benefits of both expert system technology and web technology. The use of web services to deliver functionalities of WBESs allows the integration of these systems in web-portals. WBESs are used in a diversity of areas like engineering, management, medicine, agriculture, education, tourism, finance etc. A study on the various features of WBESs like knowledge-representation, reasoning, languages, implementation tools, use of various web services-related processes such as discovery, selection, composition etc. can result into some interesting conclusions. Presented work tries to fulfill the same purpose. The paper presents various WBESs classified according to their use of domain. The comparisons, observations, and assessments of these systems are presented by emphasizing the above-mentioned features of WBESs. A discussion on different web services-related processes and some representative techniques for performing each has also been provided to clarify their use in the WBESs. On the basis of assessments and surveys from different perspectives, some remarkable conclusions are drawn.
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Tavana, Madjid, and Vahid Hajipour. "A practical review and taxonomy of fuzzy expert systems: methods and applications." Benchmarking: An International Journal 27, no. 1 (2019): 81–136. http://dx.doi.org/10.1108/bij-04-2019-0178.

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Purpose Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems. Design/methodology/approach The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems. Findings The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field. Originality/value Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.
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Gao, Shengxiang, Zhengtao Yu, Linbin Shi, Xin Yan, and Haixia Song. "A Method to Review Expert Recommendation Using Topic Relevance and Expert Relationship." International Journal of Cooperative Information Systems 27, no. 01 (2018): 1741004. http://dx.doi.org/10.1142/s0218843017410040.

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In the process of recommending review experts to projects, in order to effectively make use of the relevance among topics and the relationship among experts, a new method is proposed for review expert recommendation using topic relevance and expert relationship. In this method, firstly, the relevance among topics and the relationships among experts are used to respectively construct the Markov network of topics and the Markov network of experts. Next, the maximum topic clique is extracted from the topic Markov network and the maximum expert clique is extracted from the expert Markov network; then, with the information of the two maximum cliques, the relevance between experts and projects is calculated. After that, according to the descending order of the relevant degree, the candidates are ranked. Finally, the experts, who are the top N to projects, are recommended. The experiments on five domain datasets are made and the results show that the proposed method can improve the effect of review expert recommendation, and the F-value increases by an average of 5% than without considering the relevance among topics and the relationship among experts.
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34

Place, John F., Alain Truchaud, Kyoichi Ozawa, Harry Pardue, and Paul Schnipelsky. "Use of artificial intelligence in analytical systems for the clinical laboratory." Journal of Automatic Chemistry 17, no. 1 (1995): 1–15. http://dx.doi.org/10.1155/s1463924695000010.

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The incorporation of information-processing technology into analytical systems in the form of standard computing software has recently been advanced by the introduction of artificial intelligence (AI), both as expert systems and as neural networks.This paper considers the role of software in system operation, control and automation, and attempts to define intelligence. AI is characterized by its ability to deal with incomplete and imprecise information and to accumulate knowledge. Expert systems, building on standard computing techniques, depend heavily on the domain experts and knowledge engineers that have programmed them to represent the real world. Neural networks are intended to emulate the pattern-recognition and parallel processing capabilities of the human brain and are taught rather than programmed. The future may lie in a combination of the recognition ability of the neural network and the rationalization capability of the expert system.In the second part of the paper, examples are given of applications of AI in stand-alone systems for knowledge engineering and medical diagnosis and in embedded systems for failure detection, image analysis, user interfacing, natural language processing, robotics and machine learning, as related to clinical laboratories.It is concluded that AI constitutes a collective form of intellectual propery, and that there is a need for better documentation, evaluation and regulation of the systems already being used in clinical laboratories.
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Anagreh, Laila F., and Emad A. Abu-Shanab. "Voter's Intention to Use Electronic Voting Systems." International Journal of E-Business Research 13, no. 3 (2017): 67–85. http://dx.doi.org/10.4018/ijebr.2017070105.

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Although the need for comprehensive assessment of mobile health (mHealth) systems is critical, most existing research focuses solely on these systems' technical merits. The purpose of this study is to prioritize different aspects and indicators of assessing the quality of mHealth services and compare four popular Iranian mHealth systems using this framework. Statistical population of this research included experts who have more than three years of active experience in the field. Using Judgmental sampling method, statistical sample size included 28 persons who responded to questionnaires. Content validity was confirmed by an expert panel, and reliability was confirmed by an inconsistency rate of less than 0.1. The study findings show that results quality is the most important component of quality assessment for mHealth systems. It also shows that indicators of confidentiality, responsiveness and customer orientation are likewise important.
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Rodd, M. G., B. T. Cronhjort, K. Gidwani, Y. Ishida, E. Knuth, and L. Motus. "Panel Discussion: Use of Expert Systems in Process Control." IFAC Proceedings Volumes 19, no. 6 (1986): 19–20. http://dx.doi.org/10.1016/s1474-6670(17)59721-9.

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LUCKMAN, P. G., M. R. JESSEN, and R. G. GIBB. "Use of Expert Systems and GIS in Land Evaluation." New Zealand Geographer 46, no. 1 (1990): 15–20. http://dx.doi.org/10.1111/j.1745-7939.1990.tb01941.x.

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Berry, Dianne C., and Donald E. Broadbent. "Human search procedures and the use of expert systems." Current Psychological Research & Reviews 5, no. 2 (1986): 130–47. http://dx.doi.org/10.1007/bf02686609.

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LeVan, David O. "Frame representation of physical systems for expert system use." Information Sciences 37, no. 1-3 (1985): 169–77. http://dx.doi.org/10.1016/0020-0255(85)90011-8.

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Su, Mu-Chun. "Use of neural networks as medical diagnosis expert systems." Computers in Biology and Medicine 24, no. 6 (1994): 419–29. http://dx.doi.org/10.1016/0010-4825(94)90040-x.

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McCauley, Nancy, and Mohammad Ala. "The use of expert systems in the healthcare industry." Information & Management 22, no. 4 (1992): 227–35. http://dx.doi.org/10.1016/0378-7206(92)90025-b.

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42

Kiryanov, Denis Aleksandrovich. "Hybrid categorical expert system for use in content aggregation." Программные системы и вычислительные методы, no. 4 (April 2021): 1–22. http://dx.doi.org/10.7256/2454-0714.2021.4.37019.

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The subject of this research is the development of the architecture of an expert system for distributed content aggregation system, the main purpose of which is the categorization of aggregated data. The author examines the advantages and disadvantages of expert systems, a toolset for the development of expert systems, classification of expert systems, as well as application of expert systems for categorization of data. Special attention is given to the description of the architecture of the proposed expert system, which consists of a spam filter, a component for determination of the main category for each type of the processed content, and components for the determination of subcategories, one of which is based on the domain rules, and the other uses the methods of machine learning methods and complements the first one. The conclusion is made that an expert system can be effectively applied for the solution of the problems of categorization of data in the content aggregation systems. The author establishes that hybrid solutions, which combine an approach based on the use of knowledge base and rules with the implementation of neural networks allow reducing the cost of the expert system. The novelty of this research lies in the proposed architecture of the system, which is easily extensible and adaptable to workloads by scaling existing modules or adding new ones.
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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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Ritter, Noah, and Jeremy Straub. "Implementation of Hardware-Based Expert Systems and Comparison of Their Performance to Software-Based Expert Systems." Machines 9, no. 12 (2021): 361. http://dx.doi.org/10.3390/machines9120361.

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Expert systems are a form of highly understandable artificial intelligence that allow humans to trace the decision-making processes that are used. While they are typically software implemented and use an iterative algorithm for rule-fact network processing, this is not the only possible implementation approach. This paper implements and evaluates the use of hardware-based expert systems. It shows that they work accurately and can be developed to parallel software implementations. It also compares the processing speed of software and hardware-based expert systems, showing that hardware-based systems typically operate two orders of magnitude faster than the software ones. The potential applications that hardware-based expert systems can be used for and the capabilities that they can provide are discussed.
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Yang, Xue Kun. "Study on Application of Expert Systems in PID Thermostatic Control." Applied Mechanics and Materials 716-717 (December 2014): 1703–6. http://dx.doi.org/10.4028/www.scientific.net/amm.716-717.1703.

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Environmental parameter control is a common case in the technique of computer measurement and control, which has been widely applied to the practice of industrial and agricultural production. PID refers to one of the algorithms commonly used in temperature control, while the setting of PID parameters is the key to the algorithm. As one of the most successful branches in artificial intelligence technology application, expert system can simulate the thinking process of human experts by making use of the experience and expertise accumulated by experts for years, so as to resolve difficult problems that can only be settled by experts. An application scheme of expert system to PID thermostatic control is proposed by this paper for the purpose of making thermostatic control more convenient and intelligent.
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46

Candra Permana, Baiq Andriska, Muhammad Djamaluddin, Muhammad Afandi, and Hariman Bahtiar. "Penerapan Sistem Pakar Untuk Diagnosa Penyakit Kucing Pada Aplikasi Berbasis Android Dengan Metode Forward Cahining." Infotek : Jurnal Informatika dan Teknologi 5, no. 1 (2022): 93–98. http://dx.doi.org/10.29408/jit.v5i1.4444.

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Technological advances can be used in various sectors of life, one of the benefits of this technology is the use of android in expert systems. Expert systems can replace the role of an expert where users can get information or solutions to a problem without having to meet directly with an expert. One of the several uses of expert systems is to diagnose disease in animals early. Cats are cute and adorable animals that are commonly kept by many people. However, cats are often affected by diseases caused by viruses, bacteria or parasites. It is very important for cat owners to find out the disease suffered by the cat early. The use of the forward chaining method applied to an android-based cat disease expert system can be used by cat keepers to detect disease in cats early , and with this application owners can save time and energy costs because they get cat disease information faster without having to meet directly with experts
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47

Silverman, Barry G., and R. Gregory Wenig. "Engineering experts critics for cooperative systems." Knowledge Engineering Review 8, no. 4 (1993): 309–28. http://dx.doi.org/10.1017/s0269888900000321.

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AbstractKnowledge collection systems often assume they are cooperating with an unbiased expert. They have few functions for checking and fixing the realism of the expertise transferred to the knowledge base, plan, document or other product of the interaction. The same problem arises when human knowledge engineers interview experts. The knowledge engineer may suffer from the same biases as the domain expert. Such biases remain in the knowledge base and cause difficulties for years to come.To prevent such difficulties, this paper introduces the reader to “critic engineering”, a methodology that is useful when it is necessary to doubt, trap and repair expert judgment during a knowledge collection process. With the use of this method, the human expert and knowledge-based critic form a cooperative system. Neither agent alone can complete the task as well as the two together.The methodology suggested here offers a number of extensions to traditional knowledge engineering techniques. Traditional knowledge engineering often answers the questions delineated in generic task (GT) theory, yet GT theory fails to provide four additional sets of questions that one must answer to engineer a knowledge base, plan, design or diagnosis when the expert is prone to error. This extended methodology is called “critic engineering”.
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48

M Abdelsalam, Hassan, Muhammad Nazri Borhan, Abdalrhman Milad, et al. "Web-Based Knowledge Evolution for Thinking Green Transportation Using Expert Systems." International Journal of Engineering & Technology 7, no. 2.29 (2018): 837. http://dx.doi.org/10.14419/ijet.v7i2.29.14267.

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In many developing nations, it is difficult for development planners and novice engineers to determine the point of transportation service and usability based only on the master plan of the city. On the other hand, nowadays managers in the organizations use computerized support for making decisions on a day-to-day basis. The experts are capable of identifying problems easily and finding solutions fast but novice engineers are unable to do so. This expert system is designed to fulfill the gap that exist between the urban design experts and user who wish to use Green transportation system namely students, decision makers and engineers. Verification, process validation and comparison of the system with similar systems showed that the system is reliable and practical. In order to create the expert system, sources related to Expert system concerning transportation have been studied and interviews with experts have been conducted. The proposed expert system is developed using Visual Studio as the building tool and SQL as the query tool. The knowledge base of thinking green transportation [T Green T] includes three modules and each module may contain a number of sub-modules, parameters, strategies, and criteria. The system incorporate rules and images for various types of measurements. Lastly, several recommendations for maintaining and improving the system are suggested. The development’s suitability can be characterized using websites of other modes of transportation such as bicycles and pedestrian traffic.
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TSAI, W. T., I. A. ZUALKERNAN, and SHEKHAR KIRANI. "PRAGMATIC TESTING METHODS FOR EXPERT SYSTEMS." International Journal on Artificial Intelligence Tools 02, no. 02 (1993): 181–217. http://dx.doi.org/10.1142/s0218213093000114.

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Effective testing of expert systems is an important but difficult task. In this paper we discuss why testing of expert systems is hard. For testing expert systems, testing techniques from conventional software engineering are adapted as a solution to these problems. We discuss the feasibility and application of black-box, white-box, and life-cycle testing techniques to expert systems. Black-box techniques include random, input partition and output partition testing. White-box techniques include path based partition, cause-effect graph, dynamic-flow, data-flow, and ablation testing. The use of these methods is demonstrated by applying the techniques to an industrial expert system. The results obtained are 1) random testing is feasible for expert systems with a small input space, 2) effectiveness of partition testing depends on the partition criteria, however, partition testing points out failure-prone partitions, 3) cause-effect testing is suitable for large expert systems if it is well decomposed, 4) dynamic-flow testing depends on the number of paths in solution and design specifications, 5) data-flow testing is feasible only if define-use pairs are obtained automatically, 6) ablation testing helps in pointing out discrepancies between specification and implementation. The overall result is that the expert systems can be treated as any other software and all the conventional software engineering testing techniques can be used to test expert systems.
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Nguyen, Hung T., Vladik Kreinovich, and Qiang Zuo. "Interval-Valued Degrees of Belief: Applications of Interval Computations to Expert Systems and Intelligent Control." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 05, no. 03 (1997): 317–58. http://dx.doi.org/10.1142/s0218488597000257.

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Usually, expert systems use numbers to describe the experts' degree of belief in their statements. In practice, however, it is difficult to assign an exact numerical value to the expert's degree of belief. At best, we can get an interval of possible values. This fact leads to the use of interval-valued degree of belief. When intervals are used to describe degrees of belief, then computations with intervals must be used to process them. In this paper, we describe applications of such interval computations to expert systems and to intelligent control.
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