Lifted MEU by Weighted Model Counting

Authors

  • Udi Apsel Ben-Gurion University of The Negev
  • Ronen Brafman Ben-Gurion University of The Negev

DOI:

https://doi.org/10.1609/aaai.v26i1.8396

Keywords:

Lifted Inference, Decision Making, Influence Diagrams

Abstract

Recent work in the field of probabilistic inference demonstrated the efficiency of weighted model counting (WMC) enginesfor 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.

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Published

2021-09-20

How to Cite

Apsel, U., & Brafman, R. (2021). Lifted MEU by Weighted Model Counting. Proceedings of the AAAI Conference on Artificial Intelligence, 26(1), 1861-1867. https://doi.org/10.1609/aaai.v26i1.8396