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Journal articles on the topic 'Expert systems (Computer science) – Design'

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Published: 4 June 2021
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1

Ruttkay, Z. "Expert systems in computer-aided design." Computer-Aided Design 21, no. 9 (November 1989): 596. http://dx.doi.org/10.1016/0010-4485(89)90022-5.

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2

Roberts, H. J. "Expert Systems Clubs: Design Methods." Computer Journal 33, no. 6 (June 1, 1990): 556–61. http://dx.doi.org/10.1093/comjnl/33.6.556.

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3

Rosenman, Michael A., John S. Gero, Peter J. Hutchinson, and Rivka Oxman. "Expert systems applications in computer-aided design." Computer-Aided Design 18, no. 10 (December 1986): 546–51. http://dx.doi.org/10.1016/0010-4485(86)90043-6.

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4

Rosenman, Michael A., John S. Gero, Peter J. Hutchinson, and Rivka Oxman. "Expert systems applications in computer-aided design." Computer-Aided Design 18, no. 7 (September 1986): 392–93. http://dx.doi.org/10.1016/0010-4485(86)90248-4.

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5

Catháin, Conall Ó. "Expert systems and design." Design Studies 8, no. 2 (April 1987): 58–61. http://dx.doi.org/10.1016/0142-694x(87)90001-9.

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6

Maher, Mary Lou. "Expert Systems for Structural Design." Journal of Computing in Civil Engineering 1, no. 4 (October 1987): 270–83. http://dx.doi.org/10.1061/(asce)0887-3801(1987)1:4(270).

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7

Lirov, Yuval, and Benjamin Melamed. "Expert design systems for telecommunications." Expert Systems with Applications 2, no. 2-3 (January 1991): 219–28. http://dx.doi.org/10.1016/0957-4174(91)90119-y.

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8

RYCHENER, MICHAEL D. "Expert systems for engineering design." Expert Systems 2, no. 1 (January 1985): 30–44. http://dx.doi.org/10.1111/j.1468-0394.1985.tb00448.x.

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9

Carroll, John M., and Jean McKendree. "Interface design issues for advice-giving expert systems." Communications of the ACM 30, no. 1 (January 1987): 14–32. http://dx.doi.org/10.1145/7885.7886.

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10

Morris, Lynn. "Maintenance-oriented design of expert systems." ISA Transactions 28, no. 1 (January 1989): 23–26. http://dx.doi.org/10.1016/0019-0578(89)90052-9.

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11

BASU, ANUPAM, ARUN K. MAJUMDAR, and SATYABROTO SINHA. "Design of industrial regulators using expert systems approach." International Journal of Systems Science 22, no. 3 (March 1991): 551–77. http://dx.doi.org/10.1080/00207729108910633.

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12

Rowley, Kurt. "Inquiry into the Practices of Expert Courseware Designers: A Pragmatic Method for the Design of Effective Instructional Systems." Journal of Educational Computing Research 33, no. 4 (December 2005): 419–50. http://dx.doi.org/10.2190/9mlr-artq-bd1p-ketn.

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A multi-stage study of the practices of expert courseware designers was conducted with the final goal of identifying methods for assisting non-experts with the design of effective instructional systems. A total of 25 expert designers were involved in all stages of the inquiry. A model of the expert courseware design process was created, tested, and refined through four try-outs. The final version of the model included instructional design task descriptions and electronic worksheets. The study revealed a common expert process that included the use of rapid prototypes, an opportunistic method of applying 14 key instructional design tasks, 8 success factors related to courseware design, and design guidelines for 10 preferred instructional strategies for courseware.
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13

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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14

Rehak, Daniel R., and H. Craig Howard. "Interfacing expert systems with design databases in integrated CAD systems." Computer-Aided Design 17, no. 9 (November 1985): 443–54. http://dx.doi.org/10.1016/0010-4485(85)90292-1.

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15

Park, Jin-Hyung, and Richard L. Storch. "Overview of ship-design expert systems." Expert Systems 19, no. 3 (July 2002): 136–41. http://dx.doi.org/10.1111/1468-0394.00199.

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16

Panjkovic, Vladimir. "Cooperating expert systems in mechanical design." Control Engineering Practice 3, no. 2 (February 1995): 294–95. http://dx.doi.org/10.1016/0967-0661(95)90088-8.

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17

Robert, Benoît, Mohamed Taleb, and Claude Marche. "Développement et usage d'un système expert pour le choix des évacuateurs de crues." Canadian Journal of Civil Engineering 19, no. 5 (October 1, 1992): 847–54. http://dx.doi.org/10.1139/l92-096.

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Expert systems are computer tools allowing the management of nonnumerical, qualitative knowledge. In that way, they depart from the numerical tools used for solving complex equation systems in computer assisted design. The integration of both types of tools is therefore desirable. Typically, the design of spillways involves long and tedious calculations, dependant on the type and nature of the structure. An expert system was developed to assist the design engineer in the initial and crucial task of choosing the baseline conditions. This expert system is based solely on technical criterion and integrates the knowledge of several experts in the field. A management tool for this multiple-source knowledge was therefore developed and integrated to the many design criterions. The system, currently being developed and implemented in an industry, was tested against approximately 40 worldwide existing structures. The responses, very promising, are presented with the system structure and its technical content. Key words: expert system, design, spillway, hydraulics.
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18

Gero, John. "Expert systems in CAD." Computer-Aided Design 17, no. 9 (November 1985): 396–98. http://dx.doi.org/10.1016/0010-4485(85)90286-6.

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19

GHOSH, D. K., and V. KALYANARAMAN. "A Relational Database Interface for Design Expert Systems." Computer-Aided Civil and Infrastructure Engineering 5, no. 2 (November 6, 2008): 151–56. http://dx.doi.org/10.1111/j.1467-8667.1990.tb00050.x.

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20

Lee, Jae K., Min Soo Suh, and Mark S. Fox. "Contingencies for the design of scheduling expert systems." Expert Systems with Applications 6, no. 3 (July 1993): 219–30. http://dx.doi.org/10.1016/0957-4174(93)90051-7.

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21

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

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22

Kathawala, Yunus. "Expert Systems: Implications for Operations Management." Industrial Management & Data Systems 90, no. 6 (June 1, 1990): 12–16. http://dx.doi.org/10.1108/02635579010004161.

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Several examples of successful expert systems applications are presented. Examples of expert systems as applied in process planning, operations planning, inventory control, process design, quality control and scheduling are covered, and the performance of these expert systems is described. Expert systems will become an essential part of computer‐integrated manufacturing (CIM) and flexible manufacturing systems (FMS) because they can perform several of the tasks mentioned above.
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23

Hammerton, J. C. "Artificial intelligence and the design of expert systems [Book Review]." Computer 22, no. 9 (September 1989): 118. http://dx.doi.org/10.1109/mc.1989.1265744.

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24

Duhovnik, J., and R. Zavbi. "Expert systems in conceptual phase of mechanical engineering design." Artificial Intelligence in Engineering 7, no. 1 (January 1992): 37–46. http://dx.doi.org/10.1016/0954-1810(92)80005-b.

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25

Davies, B. J., I. L. Darbyshire, and A. J. Wright. "Expert systems in process planning." Computer-Aided Design 18, no. 7 (September 1986): 389. http://dx.doi.org/10.1016/0010-4485(86)90227-7.

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26

Howard, H. C., and D. R. Rehak. "Expert systems and CAD databases." Computer-Aided Design 18, no. 7 (September 1986): 393. http://dx.doi.org/10.1016/0010-4485(86)90249-6.

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27

Wagenknecht, Thomas, René Filpe, and Christof Weinhardt. "Towards a design theory of computer-supported organizational participation." Journal of Enterprise Information Management 30, no. 1 (February 13, 2017): 188–202. http://dx.doi.org/10.1108/jeim-01-2016-0007.

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Purpose Employees demand high responsibility and empowerment, while keeping their work communal and flexible. Initiatives that foster organizational participation (OP) can contribute to the fulfillment of such work conditions. Research in sociology and psychology demonstrated positive effects on job satisfaction as well as on productivity. However, adoption of social software is widely spread in firms, research on the determinants of computer-supported OP is scarce. The purpose of this paper is to describe the elements to consider when designing OP processes that aim to be beneficial for both the employer as well as the employees. Design/methodology/approach The authors conducted 20 guided expert interviews to propose a nascent design theory, following a socio-technical approach that promotes democratic and humanistic principles. Findings Building on the expert interview, the process model includes a topic horizon and a collaboration phase, which creates proposals that have to be decided in order to produce results. The authors show how employee competence and leadership commitment are as important as the workload and support as well as an option for anonymous communication. The authors propose a set of features and explain principles of implementation. Research limitations/implications Despite the authors’ best efforts to diversify the authors’ set of experts, the findings have a limited generalizability as the authors only interviewed a few selected German experts that were either members of the board, HR or IT managers, often concerned with organizing rather than only participating in computer-supported organizational participation (CSOP) processes. Besides testing the model in practise, future research should also consider surveying a broader (and more international) set of employers and employees. Practical implications The authors propose a step-by-step procedure to introduce CSOP. Despite identifying many pitfalls, the research demonstrates that CSOP promises a wide variety of benefits to both employers as well as the employees of an organization, including increased satisfaction as well as productivity. Originality/value This is one of the first studies to propose a nascent design theory for CSOP. The authors derive a number of requirements to consider when implementing an information management system that seeks to improve both the efficiency and equality of employers and employees and lead to a win-win situation for both. The authors describe valid constructs for firms with spatially and timely dispersed teams and more than 50 employees. The research is based on 20 expert interviews, conducted with senior managers of medium and large German enterprises.
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28

Sakr, K. M., and M. U. Hosain. "Development of a hybrid expert system: a case study." Canadian Journal of Civil Engineering 19, no. 5 (October 1, 1992): 744–50. http://dx.doi.org/10.1139/l92-085.

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This paper considers the possible enhancement of the capabilities of an expert system tool. Most of the commercial tools currently available are not particularly suitable for developing expert systems that involve routine design calculations. To overcome this drawback for engineering applications, numerical computations may be carried out by conventional computer programs which can be linked to an expert system tool through its external program interface. This type of expert systems is called a "hybrid" or "coupled" expert system. Practicing engineers will, sooner or later, face the need to use hybrid expert systems. This paper presents a case study which shows how the capabilities of a commercial expert system tool can be enhanced by integrating it with a conventional computer program. The hybrid expert system developed by the authors for illustration purpose can be used for the analysis of plane steel trusses and the evaluation of member design forces. It utilizes an in-house program called “Manager” to integrate two commercial software packages: an expert system tool called KES and a structural analysis package named PFRAME. The capabilities of the hybrid system appear to exceed those of the individual software packages. Key words: knowledge based expert systems, expert system building tools, hybrid expert systems, structural analysis, structural design.
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29

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

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30

Maher, Mary Lou. "HI-RISE and beyond: directions for expert systems in design." Computer-Aided Design 17, no. 9 (November 1985): 420–27. http://dx.doi.org/10.1016/0010-4485(85)90289-1.

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31

Furuta, Hitoshi, King-Sun Tu, and James T. P. Yao. "Structural engineering applications of expert systems." Computer-Aided Design 17, no. 9 (November 1985): 410–19. http://dx.doi.org/10.1016/0010-4485(85)90288-x.

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32

Jain, D., and M. L. Mahe. "Combining expert systems and CAD techniques." Computer-Aided Design 21, no. 3 (April 1989): 184. http://dx.doi.org/10.1016/0010-4485(89)90087-0.

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33

BAHNASAWI, AHMED A., SHAWKI Z. EID, and HESHAM A. HEFNY. "EXPMUL: an expert system for the design of multivariable control systems." International Journal of Systems Science 24, no. 12 (December 1993): 2345–66. http://dx.doi.org/10.1080/00207729308949633.

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34

Hunt, Ruston M., and Paul R. Frey. "Knowledge Aided Display Design (KADD) System: An Evaluation." Proceedings of the Human Factors Society Annual Meeting 31, no. 5 (September 1987): 536–40. http://dx.doi.org/10.1177/154193128703100512.

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The development and evaluation of the Knowledge Aided Display Design (KADD) system is described. Developed to investigate several designer support concepts in the context of the design of computer-generated displays, KADD's implementation uses technology from several disciplines of computer science including data base design and management, graphics, expert systems, and real-time simulation. This paper discusses KADD's goals and concepts, the implementation of the system, and the results of a two-part evaluation to determine the effectiveness of the KADD concepts.
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35

CASTILLO, ENRIQUE, and ELENA ALVAREZ. "Uncertainty Methods in Expert Systems." Computer-Aided Civil and Infrastructure Engineering 5, no. 1 (November 6, 2008): 43–58. http://dx.doi.org/10.1111/j.1467-8667.1990.tb00040.x.

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36

Jubete, F., and E. Castillo. "Linear Programming and Expert Systems." Computer-Aided Civil and Infrastructure Engineering 9, no. 5 (September 1994): 335–46. http://dx.doi.org/10.1111/j.1467-8667.1994.tb00341.x.

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37

Alvarez, E., and E. Castillo. "Uncertainty Measures in Expert Systems." Computer-Aided Civil and Infrastructure Engineering 9, no. 5 (September 1994): 359–66. http://dx.doi.org/10.1111/j.1467-8667.1994.tb00343.x.

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38

Dimitropoulos, Anastasios. "Deriving a construct from site specific data: a knowledge level analysis." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 6, no. 3 (August 1992): 163–76. http://dx.doi.org/10.1017/s089006040000305x.

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In wood engineering design, an important task is the derivation of a construct from site specific data. Human experts perform the task in two phases, first qualitatively and then quantitatively in a hierarchical fashion. COWEN (Computer Wood ENgineer) is a fully implemented research prototype expert system that performs the qualitative phase and makes two contributions to the technology of expert systems. The first contribution is a Knowledge Level specification of the task prior to considering Symbol Level implementation. This is important because expert systems have been defined as mostly symbolic processors in the literature. The second contribution is that this Knowledge Level specification has led to the conclusion that additional qualitative sciences, besides physics and geometry, are needed for an engineering task. This is an interesting discovery because qualitative reasoning research in Artificial Intelligence (AI) has approached engineering design from the viewpoints of physics and geometry only.
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39

Thurston, D. L., and C. A. Crawford. "A Method for Integrating End-User Preferences for Design Evaluation in Rule-Based Systems." Journal of Mechanical Design 116, no. 2 (June 1, 1994): 522–30. http://dx.doi.org/10.1115/1.2919410.

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Expert systems for design often include provisions for comparison of preliminary design alternatives. Historically, this task has been done on an ad hoc basis (or not at all) due to two difficulties. The first difficulty is design evaluation of multiple attributes. The second is that of taking into account highly subjective end-user preferences. Design experts have developed techniques which have enabled them to deal with these two difficulties; weighted average methods for the former and heuristic “rules of thumb” which categorize end-users for the latter. Limitations of these techniques are that the accuracy and precision of weighted average methods is inadequate, and that the “rules of thumb” might be reasonable and valid for most end-users, but not for some others. This paper brings quantitative rigor to the modelling of end-user preferences which is equal to that used in other phases of engineering analysis. We present a technique by which a heuristic rule base derived from technical experts can be analyzed and modified to integrate quantitative assessment of end-users’ subjective preferences. The operations research tool of multiattribute utility analysis is integrated with artificial intelligence techniques to facilitate preliminary evaluation of design alternatives of multiple attributes specific to individual users. The steps of the methodology are: develop the heuristic rule base, analyze the rule base to separate subjective from objective rules, add a subjective multiattribute utility assessment module, add an uncertainty assessment module, make objective rules explicit, and express performance attributes in terms of design decision variables. The key step is making the distinction between subjective and objective aspects of rules, and replacing the former with utility analysis. These steps are illustrated through an expert system for materials selection for a sailboat mast. Results indicate improved expert system performance for both “typical” and “atypical” end-users.
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40

Dixon, John R., Eugene C. Libardi, Steven C. Luby, Mohan Vaghul, and Melvin K. Simmons. "Expert systems for mechanical design: Examples of symbolic representations of design geometries." Engineering with Computers 2, no. 1 (March 1987): 1–10. http://dx.doi.org/10.1007/bf01200172.

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41

Li, Tao, and Luyuan Fang. "CODAR: General purpose design tool for rule-based engineering expert systems." Artificial Intelligence in Engineering 2, no. 2 (April 1987): 76–81. http://dx.doi.org/10.1016/0954-1810(87)90141-5.

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42

Nolte, Alexander, and Michael Prilla. "Anyone can use Models." International Journal of e-Collaboration 9, no. 4 (October 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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43

Radford, A. D., and J. S. Gero. "Towards generative expert systems for architectural detailing." Computer-Aided Design 17, no. 9 (November 1985): 428–35. http://dx.doi.org/10.1016/0010-4485(85)90290-8.

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44

Smith, R. W. "Some logic modelling strategies for expert systems." Computer-Aided Design 18, no. 7 (September 1986): 393. http://dx.doi.org/10.1016/0010-4485(86)90251-4.

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45

Ajmal, A. "Clear and complete review of expert systems." Computer-Aided Design 21, no. 7 (September 1989): 469. http://dx.doi.org/10.1016/0010-4485(89)90135-8.

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46

S., Margret Anouncia, Clara Madonna L. J., Jeevitha P., and Nandhini R. T. "Design of a Diabetic Diagnosis System Using Rough Sets." Cybernetics and Information Technologies 13, no. 3 (September 1, 2013): 124–39. http://dx.doi.org/10.2478/cait-2013-0030.

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Abstract Traditionally the diagnosis of a disease is done by medical experts with experience, clinical data of the patients and adequate knowledge in identifying the disease. Such diagnosis is found to be approximate and time-consuming since it purely depends on the availability and the experience of the medical experts dealing with imprecise and uncertain clinical data of the patients. Hence, to improve decision making with uncertain data and to reduce the time consumption in diagnosing a disease, several simulated diagnosis systems have been developed. Most of these diagnosis systems are designed to possess the clinical data and symptoms associated with a specific disease as knowledge base. The quality of the knowledge base has an impact not only on the consequences, but also on the diagnostic precision. Most of the existing systems have been developed as an expert system that contains all the diagnosis facts as rules. Notably, applying the concept of a fuzzy set has shown better knowledge representation to improve the decision making process. Therefore an attempt is made in this paper to design and develop such diagnosis system, using a rough set. The system developed is evaluated using a simple set of symptoms that is added to clinical data in determining diabetes and its severity.
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47

Sagheb-Tehrani, Mehdi. "The design process of expert systems development: some concerns." Expert Systems 23, no. 2 (May 2006): 116–25. http://dx.doi.org/10.1111/j.1468-0394.2006.00328.x.

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48

Debras, Philppe, Patrice Poyet, and Eric Brisson. "Expert Systems and Documentary Dataases Integration." Computer-Aided Civil and Infrastructure Engineering 6, no. 4 (November 6, 2008): 281–89. http://dx.doi.org/10.1111/j.1467-8667.1991.tb00259.x.

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49

Castillo, E., A. Cobo, J. M. Gutiérrez, A. Iglesias, and H. Sagástegui. "Causal Network Models in Expert Systems." Computer-Aided Civil and Infrastructure Engineering 9, no. 5 (September 1994): 315–28. http://dx.doi.org/10.1111/j.1467-8667.1994.tb00339.x.

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50

Castillo, E., E. Mora, and E. Alvarez. "Log-Linear Models in Expert Systems." Computer-Aided Civil and Infrastructure Engineering 9, no. 5 (September 1994): 347–57. http://dx.doi.org/10.1111/j.1467-8667.1994.tb00342.x.

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