Uncertainty in Artificial Intelligence
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A Delayed Column Generation Strategy for Exact k-Bounded MAP Inference in Markov Logic Networks
Mathias Niepert
Abstract:
The paper introduces k-bounded MAP inference, a parameterization of MAP inference in Markov logic networks. k-Bounded MAP states are MAP states with at most k active ground atoms of hidden (non-evidence) predicates. We present a novel delayed column generation algorithm and provide empirical evidence that the algorithm efficiently computes k-bounded MAP states for meaningful real-world graph matching problems. The underlying idea is that, instead of solving one large optimization problem, it is often more efficient to tackle several small ones.
Keywords:
Pages: 384-391
PS Link:
PDF Link: /papers/10/p384-niepert.pdf
BibTex:
@INPROCEEDINGS{Niepert10,
AUTHOR = "Mathias Niepert ",
TITLE = "A Delayed Column Generation Strategy for Exact k-Bounded MAP Inference in Markov Logic Networks",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "384--391"
}


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