Jean-Marie Lagniez

gDMC: A Generic Distributed Model Counting Framework via Work-Stealing

Zhewei Xu, Minghao Yin, Jumping Zhou, Jean-Marie Lagniez

Propositional Model Counting (#SAT) is essential for probabilistic reasoning but faces scalability limits on single cores. Existing distributed approaches struggle with high initialization overheads (static decomposition) or rigid architecture. We propose a novel, generic framework for distributed exact model counting. Leveraging C++ …

cn, fr (code pays fourni par la source)

gDMC: A Generic Distributed Model Counting Framework via Work-Stealing

Zhewei Xu, Minghao Yin, Jumping Zhou, Jean-Marie Lagniez

Propositional Model Counting ($\#\mathsf{SAT}$) is essential for probabilistic reasoning but faces scalability limits on single cores. Existing distributed approaches struggle with high initialization overheads (static decomposition) or rigid architecture. We propose a novel, generic framework for distributed \emph{exact} model counting. Leveraging C++ …

cn, fr (code pays fourni par la source)

AI-Driven Surrogate Models for Predicting Electrode-Scale Discharge Behavior in Lithium-Ion Batteries

Mengda Xing, Jean-Marie Lagniez, Alejandro A. Franco

fr (code pays fourni par la source)

Efficient Minimization of Decision-DNNF Circuits via Semantic Hashing and Provenance Tracking

Jean-Marie Lagniez, Emmanuel Lonca, Armin Biere

This directory contains the supplementary material for the experiments. Softwares Two softwares are included: decdnnf-rs: The version in which we included our approach. ddnnf-to-aig: The translator from decision-DNNF to ASCII AIG. Building the Software In order to build a software, uncompress the …

de (code pays fourni par la source)

Efficient Minimization of Decision-DNNF Circuits via Semantic Hashing and Provenance Tracking

Jean-Marie Lagniez, Emmanuel Lonca, Armin Biere

This directory contains the supplementary material for the experiments. Softwares Two softwares are included: decdnnf-rs: The version in which we included our approach. ddnnf-to-aig: The translator from decision-DNNF to ASCII AIG. Building the Software In order to build a software, uncompress the …

de (code pays fourni par la source)

crillab/decdnnf_rs_c

Jean-Marie Lagniez, Emmanuel Lonca

C bindings for decdnnf-rs

fr (code pays fourni par la source)

crillab/decdnnf_rs_py

Jean-Marie Lagniez, Emmanuel Lonca

Python bindings for decdnnf_rs

fr (code pays fourni par la source)

decdnnf_rs: A Framework for Querying d-DNNF (Tool Paper)

Jean-Marie Lagniez, Emmanuel Lonca

Industrial automated reasoning demands the rapid, repeated extraction of insights from complex formulas. Knowledge compilation into the Deterministic Decomposable Negation Normal Form (d-DNNF) addresses this by reducing natively intractable tasks to polynomial-time operations. We present decdnnf_rs, a performant framework for executing advanced …

fr (code pays fourni par la source)

Counterexample-Guided Abstraction Refinement for Assumption-based Argumentation

Jean-Marie Lagniez, Emmanuel Lonca, Jean-Guy Mailly

Assumption-Based Argumentation (ABA) is a prominent formalism for structured argumentation, widely applied in domains such as healthcare, law, and robotics. Despite its inherent computational complexity, ABA has seen the development of effective techniques that successfully address key tasks, including evaluating the acceptability …

fr (code pays fourni par la source)

An Embarrassingly Parallel Model Counter

Zheng Xu, Minghao Yin, Jean-Marie Lagniez

Model counting (also known as #SAT) is a fundamental problem in knowledge representation and reasoning, with applications ranging from probabilistic inference to formal verification. However, state-of-the-art model counters are limited by computational resources on a single machine. In this paper, we propose …

cn, fr (code pays fourni par la source)

Enhancing Query Efficiency for D-DNNF Representations Through Preprocessing

Jean-Marie Lagniez, Emmanuel Lonca

fr (code pays fourni par la source)

Learning Model Agnostic Explanations via Constraint Programming

Frédéric Koriche, Jean-Marie Lagniez, Stefan Mengel, Chi-Kien Tran

Interpretable Machine Learning faces a recurring challenge of explaining the predictions made by opaque classifiers such as ensemble models, kernel methods, or neural networks in terms that are understandable to humans. When the model is viewed as a black box, the objective …