Bipartite Encoding: A New Binary Encoding for Solving Non-Binary CSPs

Abstract

Constraint Satisfaction Problems (CSPs) are typically solved with Generalized Arc Consistency (GAC). A general CSP can also be encoded into a binary CSP and solved with Arc Consistency (AC). The well-known Hidden Variable Encoding (HVE) is still a state-of-the-art binary encoding for solving CSPs. We propose a new binary encoding, called Bipartite Encoding (BE) which uses the idea of partitioning constraints. A BE encoded CSP can achieve a higher level of consistency than GAC on the original CSP. We give an algorithm for creating compact bipartite encoding for non-binary CSPs. We present a AC propagator on the binary constraints from BE exploiting their special structure. Experiments on a large set of non-binary CSP benchmarks with table constraints using the Wdeg, Activity and Impact heuristics show that BE with our AC propagator can outperform existing state-of-the-art GAC algorithms (CT, STRbit) and binary encodings (HVE with HTAC).

Cite

Text

Wang and Yap. "Bipartite Encoding: A New Binary Encoding for Solving Non-Binary CSPs." International Joint Conference on Artificial Intelligence, 2020. doi:10.24963/IJCAI.2020/165

Markdown

[Wang and Yap. "Bipartite Encoding: A New Binary Encoding for Solving Non-Binary CSPs." International Joint Conference on Artificial Intelligence, 2020.](https://mlanthology.org/ijcai/2020/wang2020ijcai-bipartite/) doi:10.24963/IJCAI.2020/165

BibTeX

@inproceedings{wang2020ijcai-bipartite,
  title     = {{Bipartite Encoding: A New Binary Encoding for Solving Non-Binary CSPs}},
  author    = {Wang, Ruiwei and Yap, Roland H. C.},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {1184-1191},
  doi       = {10.24963/IJCAI.2020/165},
  url       = {https://mlanthology.org/ijcai/2020/wang2020ijcai-bipartite/}
}