2017 IJCAI IJCAI 2017

On Neighborhood Singleton Consistencies

Abstract

CP solvers predominantly use arc consistency (AC) as the default propagation method. Many stronger consistencies, such as triangle consistencies (e.g. RPC and maxRPC) exist, but their use is limited despite results showing that they outperform AC on many problems. This is due to the intricacies involved in incorporating them into solvers. On the other hand, singleton consistencies such as SAC can be easily crafted into solvers but they are too expensive. We seek a balance between the efficiency of triangle consistencies and the ease of implementation of singleton ones. Using the recently proposed variant of SAC called Neighborhood SAC as basis, we propose a family of weaker singleton consistencies. We study them theoretically, comparing their pruning power to existing consistencies. We make a detailed experimental study using a very simple algorithm for their implementation. Results demonstrate that they outperform the existing propagation techniques, often by orders of magnitude, on a wide range of problems.

🧭 Keyword Pioneer — arc consistency
🐝 Cross-Pollinator — Artificial Intelligence, Data Science & Analytics, Deep Learning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics
🌉 Interdisciplinary Bridge — Computer Science and Mathematics & Optimization
🐣 Hot Topic Early Bird — constraint satisfaction