Uncertainty in Artificial Intelligence
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Constraint Processing in Lifted Probabilistic Inference
Jacek Kisynski, David Poole
Abstract:
First-order probabilistic models combine representational power of first-order logic with graphical models. There is an ongoing effort to design lifted inference algorithms for first-order probabilistic models. We analyze lifted inference from the perspective of constraint processing and, through this viewpoint, we analyze and compare existing approaches and expose their advantages and limitations. Our theoretical results show that the wrong choice of constraint processing method can lead to exponential increase in computational complexity. Our empirical tests confirm the importance of constraint processing in lifted inference. This is the first theoretical and empirical study of constraint processing in lifted inference.
Keywords: null
Pages: 293-302
PS Link:
PDF Link: /papers/09/p293-kisynski.pdf
BibTex:
@INPROCEEDINGS{Kisynski09,
AUTHOR = "Jacek Kisynski and David Poole",
TITLE = "Constraint Processing in Lifted Probabilistic Inference",
BOOKTITLE = "Proceedings of the Twenty-Fifth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-09)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2009",
PAGES = "293--302"
}


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