paper-with-me

홈 › Papers

diff-SAT -- A Software for Sampling and Probabilistic Reasoning for SAT and Answer Set Programming

2021-01-03 · Matthias Nickles

This paper describes diff-SAT, an Answer Set and SAT solver which combines regular solving with the capability to use probabilistic clauses, facts and rules, and to sample an optimal world-view (multiset of satisfying Boolean variable assignments or answer sets) subject to user-provided probabilistic constraints. The sampling process minimizes a user-defined differentiable objective function using a gradient descent based optimization method called Differentiable Satisfiability Solving ($\partial\mathrm{SAT}$) respectively Differentiable Answer Set Programming ($\partial\mathrm{ASP}$). Use cases are i.a. probabilistic logic programming (in form of Probabilistic Answer Set Programming), Probabilistic Boolean Satisfiability solving (PSAT), and distribution-aware sampling of model multisets (answer sets or Boolean interpretations).

📄 PDF Abstract BibTeX arXiv:2101.00589

Code (1)

MatthiasNickles/diff-SAT 공식 구현

Similar Papers 제목 키워드 기반

PiCSAR: Probabilistic Confidence Selection And Ranking for Reasoning Chains

2025-08-29 · Joshua Ong Jun Leang, Zheng Zhao, Aryo Pradipta Gema, Sohee Yang 외 arxiv

Best-of-n sampling improves the accuracy of large language models (LLMs) and large reasoning models (LRMs) by generating multiple candidate solutions and selecting the one with the highest reward. The key challenge for r…

Differentiable Satisfiability and Differentiable Answer Set Programming for Sampling-Based Multi-Model Optimization

2018-12-31 · Matthias Nickles

We propose Differentiable Satisfiability and Differentiable Answer Set Programming (Differentiable SAT/ASP) for multi-model optimization. Models (answer sets or satisfying truth assignments) are sampled using a novel SAT…

Model Optimization

PrASP Report

2016-12-30 · Matthias Nickles

This technical report describes the usage, syntax, semantics and core algorithms of the probabilistic inductive logic programming framework PrASP. PrASP is a research software which integrates non-monotonic reasoning bas…

Inductive logic programming

Optimisation in Neurosymbolic Learning Systems

2024-01-19 · Emile van Krieken

Neurosymbolic AI aims to integrate deep learning with symbolic AI. This integration has many promises, such as decreasing the amount of data required to train a neural network, improving the explainability and interpreta…

OpenEstimate: Evaluating LLMs on Reasoning Under Uncertainty with Real-World Data

2025-10-16 · Alana Renda, Jillian Ross, Michael Cafarella, Jacob Andreas arxiv

Real-world settings where language models (LMs) are deployed -- in domains spanning healthcare, finance, and other forms of knowledge work -- require models to grapple with incomplete information and reason under uncerta…