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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

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

Publication year

2020

Confidentiality

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

Article name in the collection

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

ISBN

978-0-9992411-6-5

ISSN

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e-ISSN

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Number of pages

7

Pages from-to

4252-4258

Publisher name

International Joint Conferences on Artificial Intelligence Organization

Place of publication

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Event location

Yokohama

Event date

Jul 11, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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