Hostname: page-component-77f85d65b8-mblfh Total loading time: 0 Render date: 2026-03-26T11:33:01.592Z Has data issue: false hasContentIssue false
  • English
  • Français

Knowledge compilation using constraint inheritance

Published online by Cambridge University Press:  27 February 2009

Rosemary Chabot  and
David C. Brown
Rosemary Chabot
Affiliation:
Multi-Vendor Customer Services Applied Research Group, Digital Equipment Corporation, 334 South Street, Shrewsbury, MA 01545
David C. Brown
Affiliation:
Computer Science Department, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609
Get access Rights & Permissions [Opens in a new window]

Abstract

Design knowledge is continually refined and expanded through experience. This research is concerned with design knowledge expressed as constraints. A simple learning mechanism simulates an expert designer's ability to incrementally adjust her knowledge when presented with slightly new problems. In response to unsatisfied expectations during the design process the system will examine its general knowledge about the design artifact, discover some relevant constraining knowledge, and convert that knowledge into a design constraint for future use. This process, referred to as constraint inheritance, should automatically improve the problem-solving performance.

Keywords

LearningDesignKnowledge CompilationConstraints

Information

Type
Articles
Information
AI EDAM , Volume 8 , Issue 2 , Spring 1994 , pp. 125 - 142
Copyright
Copyright © Cambridge University Press 1994

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

REFERENCES

Brachman, R.J., & Schmolze, J.G. (1989). An overview of the KL-ONE knowledge representation system. In Readings in Artificial Intelligence and Databases (Mylopoulos, J. and Brodie, M., Eds.), pp. 207–222. Morgan Kaufmann, San Mateo, CA.Google Scholar
Brown, D.C. (1985). Capturing mechanical design knowledge. Proc. ASME Int. Computers in Engineering Conf., 121–129.Google Scholar
Brown, D.C. (1989a). Compilation: The hidden dimension of design systems. Proc. 3rd IFIP WG 5.2 Workshop on Intelligent CAD, Osaka, Japan, Intelligent CAD, III (Yoshikawa, H., Arbab, F., and Tomiyama, T., Eds.), 1991, pp. 99–108. North-Holland, Amsterdam.Google Scholar
Brown, D.C. (1989b). Adjusting constraints in routine design knowledge. Preprints of the NSF Engineering Design Research Conference, UMASS, Amherst, MA.Google Scholar
Brown, D.C. (1991). Emergent themes in intelligent CAD. In Intelligent CAD, III (Yoshikawa, H. and Arbab, F., Eds.), pp. 17–19. North-Holland, Amsterdam.Google Scholar
Brown, D.C. (1994). Routineness revisited. In Mechanical Design: Theory and Methodology (Waldron, M. and Waldron, K., Eds.). Springer-Verlag, Berlin, in press.Google Scholar
Brown, D.C., & Breau, R. (1986). Types of constraints in routine design problem-solving. In Applications of Artificial Intelligence in Engineering Problems (Sriram, D. and Adey, R., Eds.), pp. 383–390. Springer-Verlag, Berlin.Google Scholar
Brown, D.C., & Chandrasekaran, B. (1989). Design Problem Solving: Knowledge Structures and Control Strategies. Morgan Kaufmann, San Mateo, CA.CrossRefGoogle Scholar
Brown, D.C., & Sloan, W.N. (1987). Compilation of design knowledge for routine design expert systems: An initial view. ASME Computers Eng. Conf., 131–136.Google Scholar
Carbonell, J.G. (1986). Derivational analogy: A theory of reconstructive problem solving and expertise acquisition. In Machine Learning: An Artificial Intelligence Approach, (Michalski, R.S., Carbonell, J.G., and Mitchell, T.M., Eds.), Vol. II, pp. 371–391. Morgan Kaufmann, San Mateo, CA.Google Scholar
Chandrasekaran, B., & Mittal, S. (1983). Deep versus compiled knowledge approaches to diagnostic problem-solving. Int. J. Man Machine Studies 19, 425–436.CrossRefGoogle Scholar
Chandrasekaran, B., & Punch, W.F. (1987). Data validation, a step beyond traditional sensor validation. Proc. 6th Natl. Conf. AI, AAAI-87, 778–782.Google Scholar
Cohen, P., & Feigenbaum, E. (Eds.) (1982). Learning from examples. In The Handbook of Artificial Intelligence, Vol. 3, p. 360. William Kaufmann, Los Altos, CA.Google Scholar
Davis, R. (1989). Expert systems: How far can they go? AI Magazine 10(2), 65–76.Google Scholar
Dixon, J.R., Cunningham, J.J., & Simmons, M.K. (1987). Research in designing with features. Proc. 1st IFIP WG 5.2 Workshop on Intelligent CAD, Intelligent CAD, I (Yoshikawa, H. and Gossard, D., Eds.), pp. 137–148. North-Holland, Amsterdam.Google Scholar
Descotte, Y., & Latombe, J.C. (1981). GARI: A problem-solver that plans how to machine parts. Proc. 7th Int. Joint Conf. AI, 766–772.Google Scholar
Edelson, D. (1992). When should a cheetah remind you of a bat? Reminding in case-based techniques. Proc. 10th Natl. Conf. Artificial Intelligence, 667–672.Google Scholar
Ellman, T. (1989). Explanation-based learning: A survey of programs and perspectives. Computing Surveys, ACM 21(2), 163–221.CrossRefGoogle Scholar
Ericsson, K.A., & Smith, J. (Eds.) (1991). Towards a General Theory of Expertise. Cambridge University Press, Cambridge.Google Scholar
Fisher, D., Subramanian, D., & Tadepalli, P. (1992). An overview of current research on knowledge compilation and speedup learning. Proc. ML92 Workshop Knowledge Compilation and Speedup Learning.Google Scholar
Gero, J.S., & Maher, M.L. (Eds.) (1993). Modelling Creativity and Knowledge-Based Creative Design. Lawrence Erlbaum, Hillsdale, NJ.Google Scholar
Ginsberg, A., Weiss, S., & Politakis, P. (1985). SEEK2: A generalized approach to automatic knowledge base refinement. Proc. 9th Int. Joint Conf. on AI, Los Angeles, Vol. 1, pp. 367–374.Google Scholar
Goel, A.K. (Ed.) (1991). IEEE Expert, Special Issue on Knowledge Compilation, 6(2).Google Scholar
Horner, R. (1989). Knowledge Compilation Using Constraint Inheritance. M.S. Thesis, Computer Science Dept., WPI.Google Scholar
Keene, S.E. (1989). Object-Oriented Programming in COMMON LISP: A Programmer's Guide to CLOS. Addison-Wesley, Reading, MA.Google Scholar
Keller, R.M., Baudin, C., Iwasaki, Y., Nayak, P., & Tanaka, K. (1989). Compiling special-purpose rules from general-purpose device models. Report No. KSL 89–49, Knowledge Systems Laboratory, Department of Computer Science, Stanford University, Stanford, CA.Google Scholar
Klein, D., & Finin, T. (1987). What's in a deep model? A characterization of knowledge depth in intelligent safety systems. Proc. 10th Int. Joint Conf. AI, 559–562.Google Scholar
Kwauk, R., & Brown, D.C. (1988). Generating and applying failure recovery suggestions in hierarchical design systems. In Artificial Intelligence in Engineering: Diagnosis and Learning (Gero, J.S., Ed.), pp. 29–50. Elsevier, Amsterdam.Google Scholar
Laird, P. (1992). Dynamic optimization. Proc. 9th Int. Workshop on Machine Learning, ML92, 263–272.CrossRefGoogle Scholar
Liu, J., & Brown, D.C. (1992). The generation of decomposition knowledge for near routine design problems. Proc. Int. Conf. Appl. AI Eng.Google Scholar
Maher, M.L. (Ed.) (1992). Preprints of the Machine Learning in Design Workshop '92, AID'92, Second Int. Conf. on AI in Design, Pittsburgh, PA.Google Scholar
Meehan, E., & Brown, D.C. (1990). Constraint absorption and relaxation using a design history. Proc. ASME Design Theory Method. Conf., 193–202.CrossRefGoogle Scholar
Minsky, M. (1975). A framework for representing knowledge. The Psychology of Computer Vision (Winston, P.H., Ed.), pp. 221–277. McGraw-Hill, New York.Google Scholar
Pazzani, M. (1986). Refining the knowledge base of a diagnostic expert system: An application of failure-driven learning. Proc. 5th Natl. Conf. AI, AAAI-86, 1029–1035.Google Scholar
Reich, Y. (1991). Design knowledge acquisition: Task analysis and a partial implementation. Knowledge Acquisition 3(3), 237–254.CrossRefGoogle Scholar
Schank, R.C. (1982). Dynamic Memory: A Theory of Reminding and Learning in Computers and People. Cambridge University Press, Cambridge.Google Scholar
Sloan, W. (1988). Constraint Migration: A Failure-Based Method of Knowledge Adjustment in Routine Design Expert System. M.S. Thesis, Computer Science Dept., WPI.Google Scholar
Spillane, M.B., & Brown, D.C. (1992). Evaluating design knowledge compilation mechanisms. In Intelligent Computer Aided Design (Brown, D.C., Waldron, M., and Yoshikawa, H., Eds.), pp. 351–373. Elsevier Science Publications B.V. (North-Holland), Amsterdam.Google Scholar
Sriram, D., & Maher, M.L. (1986). The representation and use of constraints in structural design. In Applications of Artificial Intelligence in Engineering Problems (Sriram, D. and Adey, R., Eds.), Vol. 1, pp. 355–368. Springer-Verlag, Berlin.Google Scholar
Tong, C., & Sriram, D. (1992a). Artificial Intelligence in Engineering Design, Vol. 1. Academic Press, San Diego.Google Scholar
Tong, C., & Sriram, D. (1992b). Artificial Intelligence in Engineering Design, Vol. 2. Academic Press, San Diego.Google Scholar
Stauffer, L., & Slaughterbeck-Hyde, R. (1989). The nature of constraints and their effect on quality and satisficing. Proc. ASME Design Theory Method. Conf., 1–7.CrossRefGoogle Scholar