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Causality in constraint propagation

Published online by Cambridge University Press:  27 February 2009

Walid E. Habib and
Allen C. Ward
Walid E. Habib
Affiliation:
Department of Mechanical Engineering, The University of Michigan, Ann Arbor, MI 48109, U.S.A.
Allen C. Ward
Affiliation:
Department of Mechanical Engineering, The University of Michigan, Ann Arbor, MI 48109, U.S.A.
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Abstract

This paper defines, for use in design, rules for propagating “distribution constraints” through relationships such as algebraic or vector equations. Distribution constraints are predicate logic statements about the values that physical system parameters may assume. The propagation rules take into account “variation source causality”: information about when and how the values are assigned during the design, manufacturing, and operation of the system.

Keywords

Constraint PropagationCausalityVariation

Information

Type
Articles
Information
AI EDAM , Volume 11 , Issue 5 , November 1997, pp. 419 - 433
Copyright
Copyright © Cambridge University Press 1997

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References

REFERENCES

Boettner, D. (1991). An in depth look at the labeled interval calculus and the mechanical design compiler. Master Thesis. The University of Michigan.Google Scholar
de Kleer, J. & Brown, J. (1984). A qualitative physics based on confluences. Artificial Intelligence 24, 7–83.CrossRefGoogle Scholar
de Kleer, J. & Brown, J. (1986). Theories of causal ordering. Artificial Intelligence 29, 33–61.CrossRefGoogle Scholar
Dechter, R. & Pearl, J. (1991). Directed constraint networks. Report No. R-153L. Cognitive Systems Laboratory, University of California, Los Angeles.Google Scholar
Finch, W. & Ward, A. (1995). Generalized set-propagation operations over relations of more than three variables. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 9, 231–242.CrossRefGoogle Scholar
Geiger, Paz, & Pearl, (1993). Learning simple causal structures. International Journal of Intelligence Systems 8(2), 231–247.Google Scholar
Habib, W., & Ward, A.C. (1997). Generalized set propagation operations for concurrent engineering. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 11(5), 409–417.CrossRefGoogle Scholar
Iwasaki, Y. (1988). Qualitative, causal reasoning about device behavior. Int. Workshop on Artificial Intelligence for Industrial Applications.CrossRefGoogle Scholar
Iwasaki, Y., & Simon, (1986). Causality in device behavior. Artificial Intelligence 29, 3–32.CrossRefGoogle Scholar
Iwasaki, Y., & Simon, (1994). Causality and model abstraction. Artificial Intelligence 67, 143–194.CrossRefGoogle Scholar
Moore, R.E. (1979). Methods and applications of interval analysis. SIAM.CrossRefGoogle Scholar
Rosenberg, R. & Karnopp, D. (1983). Introduction to Physical System Dynamics. McGraw-Hill, New York.Google Scholar
Ward, A. (1990). A formal system for quantitative inferences of sets of artifacts. Proc. First Int. Workshop on Formal Methods in Engineering Design, Manufacturing, and Assembly.Google Scholar