Counting and Sampling Models in First-Order Logic

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00367820" target="_blank" >RIV/68407700:21230/23:00367820 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.24963/ijcai.2023/801" target="_blank" >https://doi.org/10.24963/ijcai.2023/801</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.24963/ijcai.2023/801" target="_blank" >10.24963/ijcai.2023/801</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Counting and Sampling Models in First-Order Logic

Popis výsledku v původním jazyce

First-order model counting (FOMC) is the task of counting models of a first-order logic sentence over a given set of domain elements. Its weighted variant, WFOMC, generalizes FOMC by assigning weights to the models and has many applications in statistical relational learning. More than ten years of research by various authors has led to identification of non-trivial classes of WFOMC problems that can be solved in time polynomial in the number of domain elements. In this paper, we describe recent works on WFOMC and the related problem of weighted first-order model sampling (WFOMS). We also discuss possible applications of WFOMC and WFOMS within statistical relational learning and beyond, e.g., automated solving of problems from enumerative combinatorics and elementary probability theory. Finally, we mention research problems that still need to be tackled in order to make applications of these methods really practical more broadly.

Název v anglickém jazyce

Counting and Sampling Models in First-Order Logic

Popis výsledku anglicky

First-order model counting (FOMC) is the task of counting models of a first-order logic sentence over a given set of domain elements. Its weighted variant, WFOMC, generalizes FOMC by assigning weights to the models and has many applications in statistical relational learning. More than ten years of research by various authors has led to identification of non-trivial classes of WFOMC problems that can be solved in time polynomial in the number of domain elements. In this paper, we describe recent works on WFOMC and the related problem of weighted first-order model sampling (WFOMS). We also discuss possible applications of WFOMC and WFOMS within statistical relational learning and beyond, e.g., automated solving of problems from enumerative combinatorics and elementary probability theory. Finally, we mention research problems that still need to be tackled in order to make applications of these methods really practical more broadly.

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

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence

ISBN

978-1-956792-03-4

ISSN

—

e-ISSN

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Počet stran výsledku

6

Strana od-do

7020-7025

Název nakladatele

International Joint Conferences on Artificial Intelligence Organization

Místo vydání

—

Místo konání akce

Macao

Datum konání akce

19. 8. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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