ML Anthology

Efficient and Complete FD-Solving for Extended Array Constraints

Quentin Plazar, Mathieu Acher, Sébastien Bardin, Arnaud Gotlieb
IJCAI 2017 pp. 1231-1238
doi:10.24963/IJCAI.2017/171 /ijcai/2017/plazar2017ijcai-efficient/

Abstract

Array constraints are essential for handling data structures in automated reasoning and software verification. Unfortunately, the use of a typical finite domain (FD) solver based on local consistency-based filtering has strong limitations when constraints on indexes are combined with constraints on array elements and size. This paper proposes an efficient and complete FD-solving technique for extended constraints over (possibly unbounded) arrays. We describe a simple but particularly powerful transformation for building an equisatisfiable formula that can be efficiently solved using standard FD reasoning over arrays, even in the unbounded case. Experiments show that the proposed solver significantly outperforms FD solvers, and successfully competes with the best SMT-solvers.

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Cite

Text

Plazar et al. "Efficient and Complete FD-Solving for Extended Array Constraints." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/171

Markdown

[Plazar et al. "Efficient and Complete FD-Solving for Extended Array Constraints." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/plazar2017ijcai-efficient/) doi:10.24963/IJCAI.2017/171

BibTeX

@inproceedings{plazar2017ijcai-efficient,
  title     = {{Efficient and Complete FD-Solving for Extended Array Constraints}},
  author    = {Plazar, Quentin and Acher, Mathieu and Bardin, Sébastien and Gotlieb, Arnaud},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2017},
  pages     = {1231-1238},
  doi       = {10.24963/IJCAI.2017/171},
  url       = {https://mlanthology.org/ijcai/2017/plazar2017ijcai-efficient/}
}