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Free logic and infinite constraint networks

  • James Bowen
  • Dennis Bahler
Session 3: Constraints
Part of the Lecture Notes in Computer Science book series (LNCS, volume 624)

Abstract

Conventional constraint systems are suitable for finding assignments to a finite set of parameters such that a set of constraints are satisfied. However, in generalized problem-solving, the set of parameters for which values must be found is only a subset of a much larger (possibly infinite) set of parameters, and the membership of this subset is dependent on conditions whose truth or falsity can only be determined dynamically. Conventional constraint systems are not suitable for such problems because conventional constraint processing requires that the set of parameters for which values are to be found should be fixed a priori. In this paper, we show how the conventional notion of constraint processing can be generalized to address the broader class of problem mentioned above.

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References

  1. 1.
    Bowen J, O'Grady P and Smith L, (1990), “A constraint programming language for life-cycle engineering,” International Journal of Artificial Intelligence in Engineering, 5, 206–220.CrossRefGoogle Scholar
  2. 2.
    Bowen J and Bahler D, 1991, “Conditional Existence of Variables in Generalized Constraint Networks,” Proc. 9th. National Conference on Artificial Intelligence (AAAI'91), 215–220.Google Scholar
  3. 3.
    Bowen J and Bahler D, 1991, “Supporting cooperation between multiple perspectives in a constraint-based approach to concurrent engineering,” Journal of Design and Manufacturing, 1, 89–105.Google Scholar
  4. 4.
    Bowen J, Bahler D and Dholakia A, 1992, “An AI constraint network-based approach to bed-of-nails DFT for digital circuit design,” in press for Computers and Electrical Engineering.Google Scholar
  5. 5.
    Colmerauer A, 1987, “An Introduction to Prolog III”. Draft, Groupe Intelligence Artificielle, Universite Aix-Marseille II, November 1987.Google Scholar
  6. 6.
    Dincbas M, Van Hentenryck P, Simonis HY, Aggoun A, Graf T and Berthier F, 1988, “The Constraint Logic Programming Language CHIP”, in Proceedings FGCS'88.Google Scholar
  7. 7.
    Friedman G and Leondes C, 1969, “Constraint Theory, Part I: Fundamentals,” IEEE Transactions on Systems Science and Cybernetics, ssc-5, 1, 48–56.Google Scholar
  8. 8.
    Hirst G, 1991, “Existence assumptions in knowledge representation,” Artificial Intelligence, 49, 199–242.CrossRefGoogle Scholar
  9. 9.
    Jaffar J and Michaylov S, 1986, “Methodology and Implementation of a CLP System”, Proceedings of the 4th International Conference on Logic Programming.Google Scholar
  10. 10.
    Lambert K and van Fraassen B, 1972, Derivation and Counterexample: An Introduction to Philosophical Logic, Enrico, CA: Dickenson Publishing Company.Google Scholar
  11. 11.
    Mackworth A, 1987. ”Constraint Satisfaction,” in S. Shapiro (ed.), The Encyclopedia of Artificial Intelligence, New York: Wiley, 205–211.Google Scholar
  12. 12.
    Mittal S and Falkenhainer B, 1990, “Dynamic Constraint Satisfaction Problems,” Proceedings of the Eighth National Conference on Artificial Intelligence, 25–32.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • James Bowen
    • 1
  • Dennis Bahler
    • 1
  1. 1.Dept. of Computer ScienceNorth Carolina State UniversityRaleighUSA

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