Hostname: page-component-7b7567c8c-wxvwp Total loading time: 0 Render date: 2026-11-14T01:20:31.519Z Has data issue: false hasContentIssue false
  • English
  • Français

An analysis of several configuration design systems1

Published online by Cambridge University Press:  27 February 2009

Alan Balkany,
William P. Birmingham and
Iris D. Tommelein
Alan Balkany
Affiliation:
Department of Electrical Engineering and Computer Science, Department of Civil and Environmental Engineering, The University of Michigan, Ann Arbor, MI 48109, U.S.A.
William P. Birmingham
Affiliation:
Department of Electrical Engineering and Computer Science, Department of Civil and Environmental Engineering, The University of Michigan, Ann Arbor, MI 48109, U.S.A.
Iris D. Tommelein
Affiliation:
Department of Electrical Engineering and Computer Science, Department of Civil and Environmental Engineering, The University of Michigan, Ann Arbor, MI 48109, U.S.A.
Get accessRights & Permissions [Opens in a new window]

Abstract

Design has been extensively studied by artificial intelligence researchers for many years. These studies have resulted in a large number of design tools that perform interesting tasks. Understanding the capabilities of these tools is, however, very difficult, which seriously impedes progress in the field. A better understanding of different tools can be achieved by analyzing the knowledge use of existing tools. Such an analysis of six configuration design tools is presented. This results in a model of configuration design that shows significant similarity in the tasks performed by these tools.

Information

Type
Research Article
Information
AI EDAM , Volume 7 , Issue 1 , February 1993, pp. 1 - 17
Copyright
Copyright © Cambridge University Press 1993

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Balkany, A., Birmingham, W. and Tommelein, I. D. 1991. A Knowledge-level analysis of several design tools. Proceedings of AI and Design '91, Butterworth Scientific Ltd.Google Scholar
Birmingham, W. P. and Tommelein, I. D. 1992. Towards a domain-independent synthesis system. In Knowledge Aided Design, ed. Green, M.London: Academic Press.Google Scholar
Birmingham, W. P., Gupta, A. P. and Siewiorek, D. P. 1989. A General Synthesis Engine: Making MICON Domain-Independent. Technical Report, EDRC, Carnegie Mellon University, EDRC-18–07–89.Google Scholar
Birmingham, W. P., Gupta, A. P. and Siewiorek, D. P. 1992. Automating the Design of Computer Systems. Boston: Jones and Bartlett.CrossRefGoogle Scholar
Brown, D. C. 1991. Routineness revisited. In Mechanical Design: Theory and Methodology, eds Waldron, M., Waldron, K.Berlin: Springer-Verlag.Google Scholar
Brown, D. C. and Chandrasekaran, B. 1985. Expert Systems for a Class of Mechanical Design Activity. IFIP, Amsterdam: Elsevier Science.Google Scholar
Brown, D. C. and Chandrasekaran, B. 1986. Knowledge and control for a mechanical design expert system. IEEE Computer, 19(7), 92–100.Google Scholar
Brown, D. C. and Chandrasekaran, B. 1989. Design Problem Solving: Knowledge Structures and Control Strategies. San Mateo, CA: Morgan Kaufmann also London: Pitman Publishing.Google Scholar
Chandrasekaran, B. 1986. Generic tasks in knowledge-based reasoning: high level building blocks for expert system design. IEEE Expert 1(3), 23–30.CrossRefGoogle Scholar
Chandrasekaran, B. 1990. Design problem solving: a task analysis. AI Magazine, 11(4), 59–71.Google Scholar
Clancey, W. J. 1985. Heuristic classification. Artificial Intelligence, 27(3), 289–350.Google Scholar
Constantine, L. and Yourdon, E. 1979. Structured Design. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Dixon, J. R. 1987. On research methodology towards a scientific theory of engineering design. Artificial Intelligence in Engineering, Design, Analysis and Manufacturing, 1(3), 145–157.Google Scholar
Frayman, F. and Mittal, S. 1987. Cossack: a constraints-based expert system for configuration tasks. The 2nd International Conference on Applications of AI to Engineering Proceedings, Boston, MA.Google Scholar
Hayes-Roth, B., Johnson, M. V., Garvey, A. and Hewett, M. 1986. Application of the BB1 blackboard architecture to arrangement-assembly tasks. International Journal for Artificial Intelligence in Engineering, 1(2), 85–94.Google Scholar
Klinker, G., Bhola, C., Dallemagne, G., Marques, D. and McDermott, J. 1990. Usable and reusable programming constructs. Proceedings of the 5th Knowledge Acquisition Workshop, AAAI.Google Scholar
Kott, A., and May, J. H. 1989. Decomposition vs. transformation: case studies of two models of the design process. Proceedings of ASME 1989 Computers in Engineering Conference, Anaheim, CA.Google Scholar
Langrana, N. A., Mitchell, T. M. and Ramachandran, N. 1986. Progress towards a knowledge-based aid for mechanical design. Symposium on Integrated and Intelligent Manufacturing, The American Society of Manufacturing Engineers.Google Scholar
Mackworth, A. K. 1977. Consistency in networks of relations. Artificial Intelligence, 8, 99–118.CrossRefGoogle Scholar
Maher, M. L. 1988. Engineering design synthesis: a domain-independent representation. Artificial Intelligence for Engineering Design, Analysis, and Manufacturing, 1(3), 207–214.CrossRefGoogle Scholar
Marcus, S., Stout, J., and McDermott, J. 1988. VT: an expert elevator designer that uses knowledge-based backtracking. AI Magazine, 9(1), 95–112.Google Scholar
McDermott, J. 1982. R1—a rule-based configurer of computer systems. Artificial Intelligence, 19(1), 39–88.Google Scholar
McDermott, J. 1988. Preliminary steps toward a taxonomy of problem-solving methods. In Automating Knowledge Acquisition for Expert Systems, ed. S., Marcus. Boston, MA: Kluwer Academic Publishers.Google Scholar
Mittal, S. and Araya, A. 1986. A knowledge-based framework for design. Proceedings of the 5th National Conference on AI. 856–865.Google Scholar
Mittal, S., Dym, C. L. and Morjaria, M. 1986. PRIDE: an expert system for the design of paper handling systems. IEEE Computer 19(7), 102–114.Google Scholar
Mittal, S. and Falkenhainer, B. 1990. Dynamic constraint satisfaction problems. Proceedings of the 8th National Conference on AI, 25–32.Google Scholar
Mittal, S. and Frayman, F. 1989. Towards a generic model of configuration tasks. Proceedings of the 11th IJCAI, 1395–1401.Google Scholar
Mostow, J. 1985. Toward better models of the design process. AI Magazine, VI(4), 44–63.Google Scholar
Musen, M. and Tu, S. 1991. A model of skeletal-plan refinement to generate task-specific knowledge-acquisition tools. Report KSL-91–05. Knowledge Systems Laboratory, Stanford University, Stanford, CA.Google Scholar
Neches, R., Fikes, R., Finin, T., Gruber, T., Patil, R., Senator, T. and Swartout, W. 1991. Enabling technology for knowledge sharing. AI Magazine, 12(3), 36–56.Google Scholar
Newell, A. 1981. The knowledge level. AI Magazine, 2(2), 1–20, 33.Google Scholar
Runkel, J., Birmingham, W. P., Darr, T., Maxim, B. and Tommelein, I. D. 1992. Domain independent design system: environment for rapid development of configuration design systems. Proceedings of AI and Design '92, Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 21–40.Google Scholar
Sriram, D., Cheong, K. and Kumar, M. 1992. Engineering design cycle: a case study and implications for CAE. In Knowledge Aided Design, ed. Green, M.London: Academic Press.Google Scholar
Steels, L. 1990. Components of expertise. AI Magazine, 11(2), 28–49.Google Scholar
Steier, D. 1990. Creating a scientific community at the interface between engineering design and AI. AI Magazine, 11(4), 18–22.Google Scholar
Tommelein, I. D. 1989. SightPlan—an expert system that models and augments human decision-making for designing construction site layouts. Department of Civil Engineering, Stanford University, Stanford, CA. Ph.D. dissertation.Google Scholar
Tommelein, I. D., Levitt, R. E., Hayes-Roth, B. and Confrey, T. 1991. SightPlan experiments: alternate strategies for site layout design. ASCE Journal of Computing in Civil Engineering, 5(1), 42–63.CrossRefGoogle Scholar
Tommelein, I. D., Levitt, R. E. and Hayes-Roth, B. 1992 a. The SightPlan model for site layout. ASCE Journal of Construction Engineering and Management, 118(4).CrossRefGoogle Scholar
Tommelein, I. D., Hayes-Roth, B. and Levitt, R. E. 1992 b. Altering the SightPlan knowledge-based system. Artificial Intelligence in Engineering, Design, Analysis and Manufacturing 6(1), 19–37.CrossRefGoogle Scholar
Tong, C. 1987. Toward an engineering science of knowledge-based design. Artificial Intelligence in Engineering, 2(3), 133–166.CrossRefGoogle Scholar
van Melle, W. 1980. A domain-indpendent production-rule system for consultation programs. Stanford University, Department of Computer Science, Report No. STAN-CS-820. Ph.D. dissertation.Google Scholar