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Unifying logical and statistical AI with Markov logic

Authors:

Pedro Domingos,

Daniel LowdAuthors Info & Claims

Communications of the ACM, Volume 62, Issue 7

Pages 74 - 83

https://doi.org/10.1145/3241978

Published: 24 June 2019 Publication History

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Abstract

Markov logic can be used as a general framework for joining logical and statistical AI.

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Index Terms

Unifying logical and statistical AI with Markov logic
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning
    1. Machine learning approaches
      1. Markov decision processes

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Published In

Communications of the ACM Volume 62, Issue 7

July 2019

87 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/3342113

Editor:
Andrew A. Chien
Association for Computing Machinery, New York, NY

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 24 June 2019

Published in CACM Volume 62, Issue 7

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Yu DLiu XYang BChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Zero-shot Image Classification with Logic Adapter and Rule PromptProceedings of the ACM Web Conference 202410.1145/3589334.3645554(2075-2084)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645554
Cui CAmiri SDing YZhan XZhang SEvans RShpitser I(2023)Learning to reason about contextual knowledge for planning under uncertaintyProceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence10.5555/3625834.3625878(465-475)Online publication date: 31-Jul-2023
https://dl.acm.org/doi/10.5555/3625834.3625878
David VFournier-S'niehotta RTravers NElkind E(2023)NeoMaPyProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/831(7123-7126)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/831
Fang HLiu YCai YSun M(2023)MLN4KB: an efficient Markov logic network engine for large-scale knowledge bases and structured logic rulesProceedings of the ACM Web Conference 202310.1145/3543507.3583248(2423-2432)Online publication date: 30-Apr-2023
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Wu MYu FLiu PHe Y(2023)A Hybrid Driving Decision-Making System Integrating Markov Logic Networks and Connectionist AIIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.322712224:3(3514-3527)Online publication date: 1-Mar-2023
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Zhang JLi LZhang CLi B(2023)CARE: Certifiably Robust Learning with Reasoning via Variational Inference2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)10.1109/SaTML54575.2023.00043(554-574)Online publication date: Feb-2023
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Li ZDuan LYue K(2023)Efficient Inference of Markov Logic Network for Link Prediction2023 IEEE 12th Global Conference on Consumer Electronics (GCCE)10.1109/GCCE59613.2023.10315508(352-353)Online publication date: 10-Oct-2023
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BELLOMARINI LLAURENZA ESALLINGER ESHERKHONOV E(2022)Swift Markov Logic for Probabilistic Reasoning on Knowledge GraphsTheory and Practice of Logic Programming10.1017/S1471068422000412(1-28)Online publication date: 9-Nov-2022
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Maruyama Y(2021)Categorical Artificial Intelligence: The Integration of Symbolic and Statistical AI for Verifiable, Ethical, and Trustworthy AIArtificial General Intelligence10.1007/978-3-030-93758-4_14(127-138)Online publication date: 15-Oct-2021
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