Cycle-Based Singleton Local Consistencies

Abstract

We propose to exploit cycles in the constraint network of a Constraint Satisfaction Problem (CSP) to vehicle constraint propagation and improve the effectiveness of local consistency algorithms. We focus our attention on the consistency property Partition-One Arc-Consistency (POAC), which is a stronger variant of Singleton Arc-Consistency (SAC). We modify the algorithm for enforcing POAC to operate on a minimum cycle basis (MCB) of the incidence graph of the CSP. We empirically show that our approach improves the performance of problem solving and constitutes a novel and effective localization of consistency algorithms. Although this paper focuses on POAC, we believe that exploiting cycles, such as MCBs, is applicable to other consistency algorithms and that our study opens a new direction in the design of consistency algorithms. This research is documented in a technical report (Woordward, Choueiry, and Bessiere 2016). http://consystlab.unl.edu/our_work/StudentReports/TR-UNL-CSE-2016-0004.pdf

Cite

Text

Woodward et al. "Cycle-Based Singleton Local Consistencies." AAAI Conference on Artificial Intelligence, 2017. doi:10.1609/AAAI.V31I1.11101

Markdown

[Woodward et al. "Cycle-Based Singleton Local Consistencies." AAAI Conference on Artificial Intelligence, 2017.](https://mlanthology.org/aaai/2017/woodward2017aaai-cycle/) doi:10.1609/AAAI.V31I1.11101

BibTeX

@inproceedings{woodward2017aaai-cycle,
  title     = {{Cycle-Based Singleton Local Consistencies}},
  author    = {Woodward, Robert J. and Choueiry, Berthe Y. and Bessiere, Christian},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2017},
  pages     = {5005-5006},
  doi       = {10.1609/AAAI.V31I1.11101},
  url       = {https://mlanthology.org/aaai/2017/woodward2017aaai-cycle/}
}