The SeqBin Constraint Revisited
Katsirelos, George, Narodytska, Nina, Walsh, Toby
–arXiv.org Artificial Intelligence
We revisit the SeqBin constraint. This meta-constraint subsumes a number of important global constraints like Change, Smooth and IncreasingNValue. We show that the previously proposed filtering algorithm for SeqBin has two drawbacks even under strong restrictions: it does not detect bounds disentailment and it is not idempotent. We identify the cause for these problems, and propose a new propagator that overcomes both issues. Our algorithm is based on a connection to the problem of finding a path of a given cost in a restricted $n$-partite graph. Our propagator enforces domain consistency in O(nd^2) and, for special cases of SeqBin that include Change, Smooth and IncreasingNValue, in O(nd) time.
arXiv.org Artificial Intelligence
Jul-7-2012
- Country:
- Europe > France
- Occitanie > Haute-Garonne > Toulouse (0.04)
- Oceania > Australia
- New South Wales > Sydney (0.04)
- Europe > France
- Genre:
- Research Report (0.40)
- Technology: