Lifting Queries for Lifted Inference

Tanya Braun; Ralf Möller

doi:10.3233/FAIA200439

Lifting Queries for Lifted Inference

Abstract

Lifted algorithms use representatives for groups of indistinguishable objects to efficiently perform inference. Standard lifted algorithms like first-order variable elimination or first-order knowledge compilation, compute answers to marginal queries of single random variables or events in a lifted way using representatives. But, queries containing a set of indistinguishable random variables may lead to groundings, something that lifting tries to avoid. This paper presents parameterised queries as a means to avoid groundings, applying the lifting idea to queries. Parameterised queries enable lifted algorithms to compute answers faster, while compactly representing queries and answers.

Originalsprache	Englisch
Titel	Frontiers in Artificial Intelligence and Applications
Redakteure/-innen	Giuseppe De Giacomo , Alejandro Catala , Bistra Dilkina , Michela Milano , Senén Barro , Alberto Bugarín , Jérôme Lang
Seitenumfang	22
Band	325
Herausgeber (Verlag)	IOS Press
Erscheinungsdatum	24.08.2020
Seiten	2891 - 2892
ISBN (Print)	978-1-64368-100-9
ISBN (elektronisch)	978-1-64368-101-6
DOIs	https://doi.org/10.3233/FAIA200439
Publikationsstatus	Veröffentlicht - 24.08.2020
Veranstaltung	24th European Conference on Artificial Intelligence - Online Streaming , Santiago de Compostela, Spanien Dauer: 29.08.2020 → 08.09.2020 Konferenznummer: 162625

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Zentren: Zentrum für Künstliche Intelligenz Lübeck (ZKIL)
Querschnittsbereich: Intelligente Systeme

UN SDGs

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@inproceedings{da47adc1255046a49883beff0420d7ad,

title = "Lifting Queries for Lifted Inference",

abstract = "Lifted algorithms use representatives for groups of indistinguishable objects to efficiently perform inference. Standard lifted algorithms like first-order variable elimination or first-order knowledge compilation, compute answers to marginal queries of single random variables or events in a lifted way using representatives. But, queries containing a set of indistinguishable random variables may lead to groundings, something that lifting tries to avoid. This paper presents parameterised queries as a means to avoid groundings, applying the lifting idea to queries. Parameterised queries enable lifted algorithms to compute answers faster, while compactly representing queries and answers.",

author = "Tanya Braun and Ralf M{\"o}ller",

note = "Publisher Copyright: {\textcopyright} 2020 The authors and IOS Press. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 24th European Conference on Artificial Intelligence , ECAI 2020 ; Conference date: 29-08-2020 Through 08-09-2020",

year = "2020",

month = aug,

day = "24",

doi = "10.3233/FAIA200439",

language = "English",

isbn = "978-1-64368-100-9 ",

volume = "325",

series = "Frontiers in Artificial Intelligence and Applications",

publisher = "IOS Press",

pages = "2891 -- 2892",

editor = "{De Giacomo }, {Giuseppe } and {Catala }, {Alejandro } and {Dilkina }, {Bistra } and {Milano }, {Michela } and {Barro }, {Sen{\'e}n } and {Bugar{\'i}n }, { Alberto } and Lang, { J{\'e}r{\^o}me }",

booktitle = "Frontiers in Artificial Intelligence and Applications",

}

Braun, T & Möller, R 2020, Lifting Queries for Lifted Inference. in G De Giacomo , A Catala , B Dilkina , M Milano , S Barro , A Bugarín & J Lang (Hrsg.), Frontiers in Artificial Intelligence and Applications. Bd. 325, Frontiers in Artificial Intelligence and Applications, IOS Press, S. 2891 - 2892, 24th European Conference on Artificial Intelligence , Santiago de Compostela, Spanien, 29.08.20. https://doi.org/10.3233/FAIA200439

Lifting Queries for Lifted Inference. / Braun, Tanya; Möller, Ralf.
Frontiers in Artificial Intelligence and Applications. Hrsg. / Giuseppe De Giacomo ; Alejandro Catala ; Bistra Dilkina ; Michela Milano ; Senén Barro ; Alberto Bugarín ; Jérôme Lang. Band 325 IOS Press, 2020. S. 2891 - 2892 (Frontiers in Artificial Intelligence and Applications).

Publikation: Kapitel in Büchern/Berichten/Konferenzbänden › Konferenzbeitrag › Begutachtung

TY - GEN

T1 - Lifting Queries for Lifted Inference

AU - Braun, Tanya

AU - Möller, Ralf

N1 - Conference code: 162625

PY - 2020/8/24

Y1 - 2020/8/24

N2 - Lifted algorithms use representatives for groups of indistinguishable objects to efficiently perform inference. Standard lifted algorithms like first-order variable elimination or first-order knowledge compilation, compute answers to marginal queries of single random variables or events in a lifted way using representatives. But, queries containing a set of indistinguishable random variables may lead to groundings, something that lifting tries to avoid. This paper presents parameterised queries as a means to avoid groundings, applying the lifting idea to queries. Parameterised queries enable lifted algorithms to compute answers faster, while compactly representing queries and answers.

AB - Lifted algorithms use representatives for groups of indistinguishable objects to efficiently perform inference. Standard lifted algorithms like first-order variable elimination or first-order knowledge compilation, compute answers to marginal queries of single random variables or events in a lifted way using representatives. But, queries containing a set of indistinguishable random variables may lead to groundings, something that lifting tries to avoid. This paper presents parameterised queries as a means to avoid groundings, applying the lifting idea to queries. Parameterised queries enable lifted algorithms to compute answers faster, while compactly representing queries and answers.

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UR - https://www.mendeley.com/catalogue/e38a0098-db98-387e-918c-7d284bd7463b/

U2 - 10.3233/FAIA200439

DO - 10.3233/FAIA200439

M3 - Conference contribution

SN - 978-1-64368-100-9

VL - 325

T3 - Frontiers in Artificial Intelligence and Applications

SP - 2891

EP - 2892

BT - Frontiers in Artificial Intelligence and Applications

A2 - De Giacomo , Giuseppe

A2 - Catala , Alejandro

A2 - Dilkina , Bistra

A2 - Milano , Michela

A2 - Barro , Senén

A2 - Bugarín , Alberto

A2 - Lang, Jérôme

PB - IOS Press

T2 - 24th European Conference on Artificial Intelligence

Y2 - 29 August 2020 through 8 September 2020

ER -

Lifting Queries for Lifted Inference

Abstract

Strategische Forschungsbereiche und Zentren

UN SDGs

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