Open-Universe Weighted Model Counting: Extended Abstract

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Weighted model counting (WMC) has recently emerged as an effective and general approach to probabilistic inference, offering a computational framework for encoding a variety of formalisms, such as factor graphs and Bayesian networks.The advent of large-scale probabilistic knowledge bases has generated further interest in relational probabilistic representations, obtained by according weights to first-order formulas, whose semantics is given in terms of the ground theory, and solved by WMC. A fundamental limitation is that the domain of quantification, by construction and design, is assumed to be finite, which is at odds with areas such as vision and language understanding, where the existence of objects must be inferred from raw data. Dropping the finite-domain assumption has been known to improve the expressiveness of a first-order language for open-universe purposes, but these languages, so far, have eluded WMC approaches. In this paper, we revisit relational probabilistic models over an infinite domain, and establish a number of results that permit effective algorithms. We demonstrate this language on a number of examples, including a parameterized version of Pearl's Burglary-Earthquake-Alarm Bayesian network.
Original languageEnglish
Title of host publicationWorkshops at the Thirty-First AAAI Conference on Artificial Intelligence 16: Symbolic Inference and Optimization
PublisherAAAI Press
Number of pages8
Publication statusPublished - 12 Feb 2017
EventThirty-First AAAI Conference on Artificial Intelligence - San Francisco, United States
Duration: 4 Feb 20179 Feb 2017

Publication series

ISSN (Print)2159-5399
ISSN (Electronic)2374-3468


ConferenceThirty-First AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI-17
Country/TerritoryUnited States
CitySan Francisco
Internet address


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