Computer Science – Artificial Intelligence
Scientific paper
2012-02-14
Computer Science
Artificial Intelligence
Scientific paper
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.
Apsel Udi
Brafman Ronen I.
No associations
LandOfFree
Extended Lifted Inference with Joint Formulas does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Extended Lifted Inference with Joint Formulas, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Extended Lifted Inference with Joint Formulas will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-90321