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).
BibTeX
@inproceedings{ijcai2020p165,
title = {Bipartite Encoding: A New Binary Encoding for Solving Non-Binary CSPs},
author = {Wang, Ruiwei and Yap, Roland H.C.},
booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
Artificial Intelligence, {IJCAI-20}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Christian Bessiere},
pages = {1184--1191},
year = {2020},
month = {7},
note = {Main track},
doi = {10.24963/ijcai.2020/165},
url = {https://doi.org/10.24963/ijcai.2020/165},
}