research-article

Probabilistic reasoning as quadratic unconstrained binary optimization

Author:

Marco BaiolettiAuthors Info & Claims

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 2177 - 2181

https://doi.org/10.1145/3520304.3534005

Published: 19 July 2022 Publication History

Abstract

Probabilistic reasoning is an important tool for using uncertainty in AI, especially for automated reasoning. Partial probability assessments are a way of expressing partial probabilistic knowledge on a set of events. These assessments contain only the information about "interesting" events (hence it can be easily assessed by an expert). On the other hand, partial assessments can cause consistency problems. In this paper we show how to formulate the main tasks of probabilistic reasoning on partial probability assessments, namely check of coherence, correction, and inference, as QUBO problems. This transformation allows to solve these problems with a quantum or a digital annealer and thus providing new computational methods to perform these tasks.

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Cited By

Baioletti MOddi ARasconi RSilva SPaquete L(2023)Solving Scheduling Problems with Quantum Computing: a Study on Flexible Open ShopProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596420(2175-2178)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3596420
Baioletti MSantini F(2022)Abstract Argumentation Goes Quantum: An Encoding to QUBO ProblemsPRICAI 2022: Trends in Artificial Intelligence10.1007/978-3-031-20862-1_4(46-60)Online publication date: 10-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-20862-1_4

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Probabilistic reasoning as quadratic unconstrained binary optimization

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cover image ACM Conferences

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2022

2395 pages

ISBN:9781450392686

DOI:10.1145/3520304

Editor:
Jonathan E. Fieldsend
University of Exeter
,
General Chair:
Markus Wagner
The University of Adelaide

Copyright © 2022 ACM.

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Published: 19 July 2022

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GECCO '22: Genetic and Evolutionary Computation Conference

July 9 - 13, 2022

Massachusetts, Boston

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Cited By

Baioletti MOddi ARasconi RSilva SPaquete L(2023)Solving Scheduling Problems with Quantum Computing: a Study on Flexible Open ShopProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596420(2175-2178)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3596420
Baioletti MSantini F(2022)Abstract Argumentation Goes Quantum: An Encoding to QUBO ProblemsPRICAI 2022: Trends in Artificial Intelligence10.1007/978-3-031-20862-1_4(46-60)Online publication date: 10-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-20862-1_4

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