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DeepPSL: end-to-end perception and reasoning

AUTHORs:

Sridhar Dasaratha,

Sai Akhil Puranam,

Karmvir Singh Phogat,

Sunil Reddy Tiyyagura,

Nigel P. DuffyAuthors Info & Claims

IJCAI '23: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence

Article No.: 401, Pages 3606 - 3614

https://doi.org/10.24963/ijcai.2023/401

Published: 19 August 2023 Publication History

Abstract

We introduce DeepPSL a variant of probabilistic soft logic (PSL) to produce an end-to-end trainable system that integrates reasoning and perception. PSL represents first-order logic in terms of a convex graphical model -- hinge-loss Markov random fields (HL-MRFs). PSL stands out among probabilistic logic frameworks due to its tractability having been applied to systems of more than 1 billion ground rules. The key to our approach is to represent predicates in first-order logic using deep neural networks and then to approximately back-propagate through the HL-MRF and thus train every aspect of the first-order system being represented. We believe that this approach represents an interesting direction for the integration of deep learning and reasoning techniques with applications to knowledge base learning, multi-task learning, and explainability. Evaluation on three different tasks demonstrates that DeepPSL significantly outperforms state-of-the-art neuro-symbolic methods on scalability while achieving comparable or better accuracy.

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Cited By

Dickens CGao CPryor CWright SGetoor LSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Convex and bilevel optimization for neural-symbolic inference and learningProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692502(10865-10896)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692502
Aspis YAlbinhassan MLobo JRusso A(2024)Embed2Rule Scalable Neuro-Symbolic Learning via Latent Space Weak-LabellingNeural-Symbolic Learning and Reasoning10.1007/978-3-031-71167-1_11(195-218)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-71167-1_11

Index Terms

DeepPSL: end-to-end perception and reasoning
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Theory of computation
  1. Logic
  2. Theory and algorithms for application domains
    1. Machine learning theory

Index terms have been assigned to the content through auto-classification.

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cover image Guide Proceedings

IJCAI '23: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence

August 2023

7242 pages

ISBN:978-1-956792-03-4

Editor:
Edith Elkind

Copyright © 2023 International Joint Conferences on Artificial Intelligence.

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Published: 19 August 2023

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Cited By

Dickens CGao CPryor CWright SGetoor LSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Convex and bilevel optimization for neural-symbolic inference and learningProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692502(10865-10896)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692502
Aspis YAlbinhassan MLobo JRusso A(2024)Embed2Rule Scalable Neuro-Symbolic Learning via Latent Space Weak-LabellingNeural-Symbolic Learning and Reasoning10.1007/978-3-031-71167-1_11(195-218)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-71167-1_11

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