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Authors: Anton R. Fuxjaeger 1 and Vaishak Belle 2 ; 1

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

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 b ut is also shown to handle a specific class of non-linear constraints over non-linear potentials. (More)

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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, SciTePress, pages 347-355. DOI: 10.5220/0008896003470355

@conference{icaart20,
author={Anton R. 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
PB - SciTePress