Approximate Weighted First-Order Model Counting: Exploiting Fast Approximate Model Counters and Symmetry

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00342408" target="_blank" >RIV/68407700:21230/20:00342408 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.24963/ijcai.2020/587" target="_blank" >https://doi.org/10.24963/ijcai.2020/587</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.24963/ijcai.2020/587" target="_blank" >10.24963/ijcai.2020/587</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Approximate Weighted First-Order Model Counting: Exploiting Fast Approximate Model Counters and Symmetry

Popis výsledku v původním jazyce

We study the symmetric weighted first-order model counting task and present ApproxWFOMC, a novel anytime method for efficiently bounding the weighted first-order model count of a sentence given an unweighted first-order model counting oracle. The algorithm has applications to inference in a variety of first-order probabilistic representations, such as Markov logic networks and probabilistic logic programs. Crucially for many applications, no assumptions are made on the form of the input sentence. Instead, the algorithm makes use of the symmetry inherent in the problem by imposing cardinality constraints on the number of possible true groundings of a sentence's literals. Realising the first-order model counting oracle in practice using the approximate hashing-based model counter ApproxMC3, we show how our algorithm is competitive with existing approximate and exact techniques for inference in first-order probabilistic models. We additionally provide PAC guarantees on the accuracy of the bounds generated.

Název v anglickém jazyce

Approximate Weighted First-Order Model Counting: Exploiting Fast Approximate Model Counters and Symmetry

Popis výsledku anglicky

We study the symmetric weighted first-order model counting task and present ApproxWFOMC, a novel anytime method for efficiently bounding the weighted first-order model count of a sentence given an unweighted first-order model counting oracle. The algorithm has applications to inference in a variety of first-order probabilistic representations, such as Markov logic networks and probabilistic logic programs. Crucially for many applications, no assumptions are made on the form of the input sentence. Instead, the algorithm makes use of the symmetry inherent in the problem by imposing cardinality constraints on the number of possible true groundings of a sentence's literals. Realising the first-order model counting oracle in practice using the approximate hashing-based model counter ApproxMC3, we show how our algorithm is competitive with existing approximate and exact techniques for inference in first-order probabilistic models. We additionally provide PAC guarantees on the accuracy of the bounds generated.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

ISBN

978-0-9992411-6-5

ISSN

—

e-ISSN

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Počet stran výsledku

7

Strana od-do

4252-4258

Název nakladatele

International Joint Conferences on Artificial Intelligence Organization

Místo vydání

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Místo konání akce

Yokohama

Datum konání akce

11. 7. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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