Cycle-Based Singleton Local Consistencies

Robert J. Woodward (1) , Berthe Y. Choueiry (1) , Christian Bessiere (2)

1 Constraint Systems Laboratory
2 COCONUT - Agents, Apprentissage, Contraintes

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).

Domaines: Intelligence artificielle [cs.AI]

Robert J. Woodward, Berthe Y. Choueiry, Christian Bessiere. Cycle-Based Singleton Local Consistencies. AAAI Conference on Artificial Intelligence, Feb 2017, San Francisco, United States. pp.5005-5006. ⟨lirmm-01711585⟩