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Annual Conference on Artificial Intelligence

KI 2011: KI 2011: Advances in Artificial Intelligence pp 122–133Cite as

Efficient Sequential Clamping for Lifted Message Passing

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7006)

Abstract

Lifted message passing approaches can be extremely fast at computing approximate marginal probability distributions over single variables and neighboring ones in the underlying graphical model. They do, however, not prescribe a way to solve more complex inference tasks such as computing joint marginals for k-tuples of distant random variables or satisfying assignments of CNFs. A popular solution in these cases is the idea of turning the complex inference task into a sequence of simpler ones by selecting and clamping variables one at a time and running lifted message passing again after each selection. This naive solution, however, recomputes the lifted network in each step from scratch, therefore often canceling the benefits of lifted inference. We show how to avoid this by efficiently computing the lifted network for each conditioning directly from the one already known for the single node marginals. Our experiments show that significant efficiency gains are possible for lifted message passing guided decimation for SAT and sampling.

Keywords

  • Relational Probabilistic Models
  • Relational Learning
  • Probabilistic Inference
  • Satisfiability

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Author information

Authors and Affiliations

  1. Knowledge Discovery Department, Fraunhofer IAIS, 53754, Sankt Augustin, Germany

    Fabian Hadiji, Babak Ahmadi & Kristian Kersting

Authors
  1. Fabian Hadiji
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  2. Babak Ahmadi
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  3. Kristian Kersting
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Editor information

Editors and Affiliations

  1. Berlin School of Mind and Brain, Humboldt-University of Berlin, Unter den Linden 6, 10099, Berlin, Germany

    Joscha Bach

  2. TZI-Technologie-Zentrum Informatik und Informationstechnik, University of Bremen, Postfach 33 04 40, 28334, Bremen, Germany

    Stefan Edelkamp

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Hadiji, F., Ahmadi, B., Kersting, K. (2011). Efficient Sequential Clamping for Lifted Message Passing. In: Bach, J., Edelkamp, S. (eds) KI 2011: Advances in Artificial Intelligence. KI 2011. Lecture Notes in Computer Science(), vol 7006. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24455-1_11

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  • DOI: https://doi.org/10.1007/978-3-642-24455-1_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24454-4

  • Online ISBN: 978-3-642-24455-1

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