Advertisement

Constraint propagation issues in automated design

  • Jean Patrick Tsang
Planning/Design/Scheduling
Part of the Lecture Notes in Computer Science book series (LNCS, volume 462)

Abstract

The Constraint Propagation paradigm is a very natural approach to automate design since it basically amounts to determining the value of a set of variables (design variables) linked by constraints. The practical enterprise of developing an automatic design system reveals a host of obstacles. In this paper, we give an account of the major problems and propose ways of tackling them. We report what we call the "boomerang effect" (which occurs when performing design by building blocks assembly) and explain how an algorithm based on it helps reduce its complexity.

Keywords

Design Variable Constraint Satisfaction Problem Constraint Propagation Constraint Network Automatic Design System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [Brillouin 62]
    Leon Brillouin Science and Information Theory Academic Press Inc, 1962Google Scholar
  2. [Brinkley 87]
    James F Brinkley, Bruce G Buchanan, Russ B Altman, Bruce S Duncan, Craig W Cornelius A Heuristic Refinement for Spatial Constraint Satisfaction Problems Stanford University Report No STAN-CS-87-1142 (KSL-87-05), Jan 1987.Google Scholar
  3. [Cooper 89]
    Martin C Cooper An Optimal k-Consistency Algorithm Artificial Intelligence 41 (1989:90) 89–95Google Scholar
  4. [Davis 87]
    Ernest Davis Constraint Propagation with Interval Labels Artificial Intelligence 32 (1987) 281–331Google Scholar
  5. [de Kleer 86]
    Johan de Kleer An Assummtion-based TMS Artificial Intelligence 28 (1986) 127–162Google Scholar
  6. [Dechter & Dechter 87]
    Avi Dechter & Rina Dechter Removing Redundancies in Constraint Networks Sixth National Conference on Artificial Intelligence, Seattle, 1987, 105–109Google Scholar
  7. [Dechter & Pearl 89]
    Rina Dechter & Judea Pearl Tree Clustering for Constraint Networks Artificial Intelligence 38 (1989) 353–366Google Scholar
  8. [Descotte & Latombe 85]
    Yannick Descotte & Jean Claude Latombe Making Compromises among Antagonist Constraints in a Planner Artificial Intelligence 27 (1985) 183–217Google Scholar
  9. [Eastman 73]
    Charles M Eastman Automated Space Planning Artificial Intelligence 4 (1973) 41–64Google Scholar
  10. [Freeman & Newell 71]
    P Freeman and A Newell A model for Functional Reasoning in Design 2nd International Conference on Artificial Intelligence, London, September 1971Google Scholar
  11. [Freuder 78]
    Eugene C Freuder Synthesizing Constraint Expressions ACM Communications, Nov 1978, Vol 21 No 11, pp 958–966Google Scholar
  12. [Gaschnig 79]
    John Gaschnig Performance Measurement and analysis of certain search algorithms PhD thesis, Department of Computer Science, CMU, 1979.Google Scholar
  13. [Han & Lee 88]
    Ching-Chih Han & Chia-Hoang Lee Comments on Mohr and Henderson's Arc and Path Consistency Algorithm Artificial Intelligence 36 (1988) 125–130Google Scholar
  14. [Haralick & Elliott 80]
    Robert M Haralick & Gordon L Elliott Increasing Tree Search Efficiency for Constraint Satisfaction Problems Artificial Intelligence 14 (1980) 263–313Google Scholar
  15. [Janssen et al 89]
    Philippe Janssen, Philippe Jégou, Bernard Nouguier & Marie-Catherine Vilarem Design Problems: an approach based on constraint satisfaction (in French) Neuvièmes Journées Internationales sur les Systèmes Experts et Leurs Applications, 71–84Google Scholar
  16. [Ingrand 87]
    François Ingrand. Inférence de Formes à partir de Fonctions. Application à la Conception de Montage d'Usinage. Thèse de Doctorat de 3ème cycle, INPG, Grenoble, 1987.Google Scholar
  17. [Laurière 78]
    Jean Louis Laurière A language and a program for stating and solving combinatorial problems Artificial Intelligence 10 (1978) 29–127Google Scholar
  18. [Mackworth 77]
    Alan K. Mackworth Consistency in networks of relations Artificial Intelligence 8 (1977) 99–118Google Scholar
  19. [Mackworth & Freuder 85]
    Alan K. Mackworth & Eugene C Freuder The complexity of some polynomial network consistency algorithms for constraint satisfaction problems Artificial Intelligence 25 (1985) 65–74Google Scholar
  20. [Mittal 86]
    Sanjay Mittal. PRIDE: An Expert System for the Design of Paper Handling Systems. IEEE computer magazine, July 1986, pp 102–114.Google Scholar
  21. [Mohr & Henderson 86]
    Roger Mohr & Thomas Henderson Arc and Path Consistency Revisited Artificial Intelligence 28 (1986) 225–233Google Scholar
  22. [Montanari 74]
    Ugo Montanari Network of constraints: Fundamental properties and applications to picture processing Information Sciences 7 (1974) 95–132Google Scholar
  23. [Nudel 85]
    Bernard Nudel Consistent-Labeling Problems and their Algorithms: Expected-Complexities and Theory-Based Heuristics Artificial Intelligence 21 (1983) 135–178Google Scholar
  24. [Pipes & Harvill 71]
    Louis A Pipes & Lawrence R Harvill Applied Mathematics for Engineers and Physicists International Student Edition, McGraw Hill Book Co, 1971Google Scholar
  25. [Sacerdoti 77]
    Earl D Sacerdoti A Structure for Plans and Behavior Elsevier North Holland, New York, 1977.Google Scholar
  26. [Stefik 80]
    Mark Jeffrey Stefik Planning with Constraints PhD thesis, Stanford University, 1980.Google Scholar
  27. [Sussman & Steele 80]
    Gerald Jay Sussman & Guy Lewis Steele Jr Constraints — A language for expressing almost-hierarchical descriptions Artificial Intelligence 14 (1980) 1–39Google Scholar
  28. [Tsang 87]
    Jean Patrick Tsang Planning by Combining Plans. Application to Process Planning Generation (in French) PhD thesis (Thèse de Docteur de l'INPG), Grenoble, 1987.Google Scholar
  29. [Tsang & Brissaud 89]
    Jean Patrick Tsang & Daniel Brissaud A Feature Based Approach to Process Planning Proceedings of the ASME International Computers in Engineering Conference, Anaheim, California, July 30–August 3, 1989, Vol 1, pp 419–430.Google Scholar
  30. [Tsang et al 90]
    Jean Patrick Tsang, Bernard Wrobel & Nathalie Pfeffer A Functional Reasoning for Automating the design process (in French) Dixièmes Journées Internationales sur les Systèmes Experts et Leurs Applications, Avignon, pp 151–166.Google Scholar
  31. [Waltz 75]
    David L Waltz Understanding line drawings of scenes with shadows in Winston PH ed, The Psychology of Computer Vision McGraw-Hill, New York 1975, pp 19–91Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1990

Authors and Affiliations

  • Jean Patrick Tsang
    • 1
  1. 1.Laboratoires de MarcoussisCGE Reserach CenterMarcoussisFrance

Personalised recommendations