Probabilistic Logic

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1 paper in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

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DPNL: A DPLL-based Algorithm for Probabilistic Neurosymbolic Learning
Oct 5, 2026Thomas Jean-Michel Valentin, Pierre Genev{è}s, Luisa Sophie Werner +3Probabilistic Logic Neurosymbolic Learning
ENS Paris Saclay,TYREX · LIG,TYREX · TYREX +1
PAA: The Probabilistic Allen Algebra: A Generative and Complete Probabilistic Extension of Allen's Interval Relations
Sep 17, 2026Julian EggertProbabilistic Logic Symbolic Reasoning
Honda Research Institute Europe, Offenbach, Germany · Honda Research Institute, Carl-Legien-Str. 30, 63073 Offenbach, Germany.
Semantic Bayesian World Models
Sep 3, 2026Tommaso SoruProbabilistic Logic World Models
Liber AI Research, London, UK
Foundations of MT-PDCL: Measure-Theoretic Probabilistic Definite Clause Logic
Aug 13, 2026Costin Bădică, Amelia BădicăProbabilistic Logic Logic Programming
Department of Computers and Information Technology University of Craiova Craiova, Romania · Department of Business Informatics University of Craiova Craiova, Romania
Structure-Preserving Uncertainty Propagation in First-Order Proof Search
Aug 10, 2026Tanel TammetProbabilistic Logic Logical Reasoning
Tallinn University of Technology
Deep probabilistic logic programming for diagnostic reasoning from incomplete information: A case study in stroke detection
Aug 9, 2026Felix Weitkämper, Monchito Avila, Elizabeth Nanjala +2Probabilistic Logic Clinical Reasoning
German University of Digital Science
From probability to causality in probabilistic logic programming
Aug 7, 2026Zora Wurm, Kilian Rückschloß, Felix WeitkämperProbabilistic Logic Causal Structure Learning
Ludwig-Maximilians-Universität München, Oettingenstraße 67, 80538 München · Eberhard-Karls-Universität Tübingen, Auf der Morgenstelle 10, 72076 Tübingen · German University of Digital Science, Marlene-Dietrich-Allee 14, 14482 Potsdam
Benchmarking LLM Competence on Logical Inference over Probability Operators
Jul 29, 2026Nayera Hasan, Jack Greff, Alvin GrissomProbabilistic Logic LLM Evaluation
Haverford College
How Rules Represent Causal Knowledge: Causal Modeling with Probabilistic Logic Programming
Jul 23, 2026Kilian Rueckschloss, Felix WeitkaemperProbabilistic Logic Logic Programming
Eberhard Karls Universitaet Tuebingen · University of Tübingen Tübingen, Germany · German University of Digital Science +1
CLARK: Closed-loop Learning for Adaptive Reasoning over Knowledge Graphs
Jul 22, 2026Yousef Khan, Luca Gherardini, Marco Maratea +2Probabilistic Logic Adaptive Inference
Computational Intelligence Team, Sano - Centre for Computational Personalised Medicine, Krakow, Poland · CISUC/LASI, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal · DeMaCS, University of Calabria, Calabria, Italy
Logic, Optimization, and Artificial Intelligence
Jul 17, 2026J. N. HookerProbabilistic Logic Logical Reasoning
Carnegie Mellon University
Probabilistic Extension of Neuro-Symbolic AGI Robots based on Belnap's Typed Intensional FOL
Jul 12, 2026Zoran MajkicProbabilistic Logic Logical Reasoning
ISRST, Tallahassee, FL, USA
DeepSWIP: Quotient-WMC Counterfactuals for Neural Probabilistic Logic Programs
Jun 18, 2026Saimun Habib, Vaishak Belle, Fengxiang HeProbabilistic Logic Structural Causal Models
University of Edinburgh
Random coloured digraphs defined by a Markov logic network
Jun 16, 2026Yasmin Tousinejad, Vera KoponenProbabilistic Logic
Department of Mathematics, Uppsala University, Sweden.
NeSyCat: A Monad-Based Categorical Semantics of the Neurosymbolic ULLER Framework
Apr 27, 2026Daniel Romero Schellhorn, Till MossakowskiProbabilistic Logic Logical Reasoning
University of Osnabrück, Osnabrück, Germany.

Oct 5, 2026, cs.AIJ/K move · Enter open · S save

DPNL: A DPLL-based Algorithm for Probabilistic Neurosymbolic Learning

Thomas Jean-Michel Valentin, Pierre Genev{è}s, Luisa Sophie Werner, Sarah Chlyah+2

ENS Paris Saclay,TYREX · LIG,TYREX · TYREX+1

Probabilistic Logic Neurosymbolic Learning

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Probabilistic Neurosymbolic Learning (PNL) combines neural predictions with symbolic reasoning, enabling end-to-end learning from final-output supervision without labels for intermediate concepts. A central challenge is probabilistic inference: state-of-the-art approaches often rely on materializing the logical provenance of a query, which can itself become a major computational bottleneck. We introduce Dynamic Probabilistic Neurosymbolic Learning (DPNL), an oracle-guided framework that avoids requiring complete provenance materialization before inference. DPNL lazily explores the space of intermediate assignments, while oracles resolve entire regions that can already be certified to produce or exclude the target output. We establish conditions ensuring soundness and termination. ApproxDPNL extends the same search with early termination while maintaining certified bounds on the exact output probability, providing controlled approximation guarantees. The oracle interface decouples inference from the representation of the symbolic component, enabling problem-specific reasoning within the same framework. Experiments on several neurosymbolic tasks show that DPNL and ApproxDPNL substantially extend the range of problem instances tractable by probabilistic neurosymbolic inference.

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