Scaling up Probabilistic Inference in Linear and Non-Linear Hybrid Domains by Leveraging Knowledge Compilation.

Anton Fuxjaeger, Vaishak Belle

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

Abstract

Weighted model integration (WMI) extends weighted model counting (WMC) in providing a computational abstraction for probabilistic inference in mixed discrete-continuous domains. WMC has emerged as an assembly language for state-of-the-art reasoning in Bayesian networks, factor graphs, probabilistic programs and probabilistic databases. In this regard, WMI shows immense promise to be much more widely applicable, especially as many real-world applications involve attribute and feature spaces that are continuous and mixed. Nonetheless, state-of-the-art tools for WMI are limited and less mature than their propositional counterparts. In this work, we propose a new implementation regime that leverages propositional knowledge compilation forscaling up inference. In particular, we use sentential decision diagrams, a tractable representation of Boolean functions, as the underlying model counting and model enumeration scheme. Our regime performs competitively to state-of-the-art WMI systems but is also shown to handle a specific class of non-linear constraints over non-linear potentials.
Original languageEnglish
Title of host publicationProceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
PublisherSCITEPRESS
Pages347-355
Number of pages9
Volume2
ISBN (Print)978-989-758-395-7
DOIs
Publication statusPublished - 24 Feb 2020
Event12th International Conference on Agents and Artificial Intelligence - Valletta, Malta
Duration: 22 Feb 202024 Feb 2020
http://www.icaart.org/

Publication series

Name
PublisherSCITEPRESS
ISSN (Electronic)2184-433X

Conference

Conference12th International Conference on Agents and Artificial Intelligence
Abbreviated titleICAART 2020
CountryMalta
CityValletta
Period22/02/2024/02/20
Internet address

Keywords

  • Weighted Model Integration
  • Probabilistic Inference
  • Knowledge Compilation
  • Sentential Decision Diagrams
  • Satisfiability Modulo Theories

Fingerprint

Dive into the research topics of 'Scaling up Probabilistic Inference in Linear and Non-Linear Hybrid Domains by Leveraging Knowledge Compilation.'. Together they form a unique fingerprint.

Cite this