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Hierarchical nonlinear constraint satisfaction
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Proceedings of the 2004 ACM symposium on Applied computing table of contents
Nicosia, Cyprus
SESSION: AI and computational logic and image analysis (AI) table of contents
Pages: 16 - 20  
Year of Publication: 2004
ISBN:1-58113-812-1
Author
Hiroshi Hosobe  National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan
Sponsor
SIGAPP: ACM Special Interest Group on Applied Computing
Publisher
ACM  New York, NY, USA
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ABSTRACT

Constraint programming is a method of problem solving that allows declarative specification of relations among objects. It is important to allow preferences of constraints since it is often difficult for programmers to specify all constraints without conflicts. In this paper, we propose a numerical method for solving nonlinear constraints with hierarcical preferences (i.e., constraint hierarchies) in a least-squares manner. This method finds sufficiently precise local optimal solutions by appropriately processing hierarchical preferences of constraints. To evaluate the effectiveness of our method, we present experimental results obtained with a prototype constraint solver.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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F. Benhamou and M. Ceberio. Soft constraints: A unifying framework applied to continuous soft constraints. In Proc. Workshop of the ERCIM Working Group on Constraints and the CoLogNET Area on Constraint and Logic Programming, 2003.
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