Extended lifted inference with joint formulas

Udi Apsel, Ronen I. Brafman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The First-Order Variable Elimination (FOVE) algorithm allows exact inference to be applied directly to probabilistic relational models, and has proven to be vastly superior to the application of standard inference methods on a grounded propositional model. Still, FOVE operators can be applied under restricted conditions, often forcing one to resort to propositional inference. This paper aims to extend the applicability of FOVE by providing two new model conversion operators: the first and the primary is joint formula conversion and the second is just-different counting conversion. These new operations allow efficient inference methods to be applied directly on relational models, where no existing efficient method could be applied hitherto.In addition, aided by these capabilities, we show how to adapt FOVE to provide exact solutions to Maximum Expected Utility (MEU) queries over relational models for decision under uncertainty. Experimental evaluations show our algorithms to provide significant speedup over the alternatives.

Original languageAmerican English
Title of host publicationProceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011
Pages11-18
Number of pages8
StatePublished - 1 Jan 2011

Publication series

NameProceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Applied Mathematics

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