Generalized Arc Consistency Algorithms for Table Constraints: A Summary of Algorithmic Ideas

Authors

  • Roland H. C. Yap National University of Singapore
  • Wei Xia National University of Singapore
  • Ruiwei Wang National University of Singapore

DOI:

https://doi.org/10.1609/aaai.v34i09.7086

Abstract

Constraint Programming is a powerful paradigm to model and solve combinatorial problems. While there are many kinds of constraints, the table constraint (also called a CSP) is perhaps the most significant—being the most well-studied and has the ability to encode any other constraints defined on finite variables. Thus, designing efficient filtering algorithms on table constraints has attracted significant research efforts. In turn, there have been great improvements in efficiency over time with the evolution and development of AC and GAC algorithms. In this paper, we survey the existing filtering algorithms for table constraint focusing on historically important ideas and recent successful techniques shown to be effective.

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Published

2020-04-03

How to Cite

Yap, R. H. C., Xia, W., & Wang, R. (2020). Generalized Arc Consistency Algorithms for Table Constraints: A Summary of Algorithmic Ideas. Proceedings of the AAAI Conference on Artificial Intelligence, 34(09), 13590-13597. https://doi.org/10.1609/aaai.v34i09.7086

Issue

Section

Senior Member Presentation Track: Summary Talks