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Types of Constraints in Routine Design Problem-Solving

  • David C. Brown
  • Robert Breau

Abstract

Our research is concerned with routine design problem-solving. By “routine” we mean that the designer has done the task many times with different requirements, so that the knowledge is now highly compiled [Chandrasekaran 1983a] [Michie 1981] [Hart 1982]. A theory of routine design and its implementation in the AIR-CYL system has been previously presented [Brown 1984] [Brown 1985b]. The theory has been incorporated into a language called DSPL that can be used to express an expert’s knowledge about routine design [Brown 1985a].

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References

  1. Brown, D.C. (August 1984) Expert Systems for Design Problem-Solving using Design Refinement with Plan Selection and Redesign. Unpublished Ph.D. Dissertation, Ohio State University, CIS Dept., OSU, Columbus, OH 43210, USA.Google Scholar
  2. Brown, D.C. and Chandrasekaran, B. (April 1985) Plan Selection in Design Problem-Solving. In: Proceedings of the AISB85 Conference, Warwick, England.Google Scholar
  3. Brown, D.C. (August 1985a) Capturing Mechanical Design Knowledge. In: Proceedings of the 1985 ASME International Computers in Engineering Conference, Boston, MA, USA.Google Scholar
  4. Brown, D.C. and Chandrasekaran, B. (November 1985b) Expert Systems for a Class of Mechanical Design Activity. In: Knowledge Engineering in Computer-Aided Design, J.S. Gero (Ed.) North Holland, pp.259–282.Google Scholar
  5. Brown, D.C. (November 1985c) Failure Handling in a Design Expert System. Computer-Aided Design, (Ed.) J.S.Gero.Google Scholar
  6. Brown, D.C. (October 1985d) A Data-Base for a Design Expert System. ACM Northeast Regional Conference. Framingham, MA, USA. {conference cancelled, but proceedings available}Google Scholar
  7. Chandrasekaran, B. (1983) Towards a taxonomy of problem-solving types. AI Magazine, AAAI, Vol.4, No.1, pp.9–17.Google Scholar
  8. Chandrasekaran, B. and Mittal, S. (1983a) Deep versus compiled knowledge approaches to diagnostic problem-solving. Int. Jnl. M. M. Studies, Vol.19, pp.425–436.CrossRefGoogle Scholar
  9. Fox, M.S., Allen, B.P., Smith, S.F. and Strohm, G.A. (1983) ISIS: A constraint-directed reasoning approach to job shop scheduling. CMU-RI-TR-83–8, Intelligent Systems Lab., Robotics Institute, Carnegie-Mellon Univ., USA.Google Scholar
  10. Hart, P. (1982) Directions for AI in the eighties. SIGART Newsletter, Vol.79, pp.11–16.Google Scholar
  11. Kelly, V.E. (1984) The CRITTER system: automated critiquing of digital circuit designs. IEEE 21st Design Automation Conf., pp.419–425.Google Scholar
  12. Michie, D. (Winter 1981/82) High-road and Low-road programs. AI magazine, Vol.3, No.l, pp.21–22.Google Scholar
  13. Smith, S.F. (1983) Exploiting temporal knowledge to organize constraints. Intelligent Systems Lab., Robotics Institute, Carnegie-Mellon Univ., USA.Google Scholar
  14. Stallman, R. and Sussman, G.J. (1977) Forward Reasoning and Dependency-directed Backtracking in a System for Computer-aided Circuit Analysis. In: Artificial Intelligence, Vol.9, pp.135–196.Google Scholar
  15. Sussman, G.J. (1977) Electrical Design — a problem for AI research. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp.894–900.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1986

Authors and Affiliations

  • David C. Brown
    • 1
  • Robert Breau
    • 1
  1. 1.Artificial Intelligence Research Group, Computer Science DepartmentWorcester Polytechnic InstituteWorcesterUSA

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