loading
Papers Papers/2020

Research.Publish.Connect.

Paper

Authors: Anton Fuxjaeger 1 and Vaishak Belle 2

Affiliations: 1 University of Edinburgh, U.K. ; 2 University of Edinburgh, U.K., Alan Turing Institute, U.K.

ISBN: 978-989-758-395-7

ISSN: 2184-433X

Keyword(s): Weighted Model Integration, Probabilistic Inference, Knowledge Compilation, Sentential Decision Diagrams, Satisfiability Modulo Theories.

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 for scaling 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 bu t is also shown to handle a specific class of non-linear constraints over non-linear potentials. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.226.72.118

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Fuxjaeger, A. and Belle, V. (2020). Scaling up Probabilistic Inference in Linear and Non-linear Hybrid Domains by Leveraging Knowledge Compilation. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-395-7 ISSN 2184-433X, pages 347-355. DOI: 10.5220/0008896003470355

@conference{icaart20,
author={Anton Fuxjaeger. and Vaishak Belle.},
title={Scaling up Probabilistic Inference in Linear and Non-linear Hybrid Domains by Leveraging Knowledge Compilation},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2020},
pages={347-355},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008896003470355},
isbn={978-989-758-395-7},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Scaling up Probabilistic Inference in Linear and Non-linear Hybrid Domains by Leveraging Knowledge Compilation
SN - 978-989-758-395-7
IS - 2184-433X
AU - Fuxjaeger, A.
AU - Belle, V.
PY - 2020
SP - 347
EP - 355
DO - 10.5220/0008896003470355

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.