Lifted MEU by Weighted Model Counting

Udi Apsel, Ronen I. Brafman

Research output: Contribution to conferencePaperpeer-review

6 Scopus citations


Recent work in the field of probabilistic inference demonstrated the efficiency of weighted model counting (WMC) engines for exact inference in propositional and, very recently, first order models. To date, these methods have not been applied to decision making models, propositional or first order, such as influence diagrams, and Markov decision networks (MDN). In this paper we show how this technique can be applied to such models. First, we show how WMC can be used to solve (propositional) MDNs. Then, we show how this can be extended to handle a first-order model - the Markov Logic Decision Network (MLDN). WMC offers two central benefits: it is a very simple and very efficient technique. This is particularly true for the first-order case, where the WMC approach is simpler conceptually, and, in many cases, more effective computationally than the existing methods for solving MLDNs via first-order variable elimination, or via propositionalization. We demonstrate the above empirically.

Original languageEnglish
Number of pages7
StatePublished - 1 Jan 2012
Event26th AAAI Conference on Artificial Intelligence, AAAI 2012 - Toronto, Canada
Duration: 22 Jul 201226 Jul 2012


Conference26th AAAI Conference on Artificial Intelligence, AAAI 2012

ASJC Scopus subject areas

  • Artificial Intelligence


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