Proceedings

Scalable Algorithms for Approximate DNF Model Counting

Paul Burkhardt, David G. Harris, Kevin T. Schmitt

Download BibTeX Cite

Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Main Track. Pages 2191-2198.

https://doi.org/10.24963/ijcai.2026/244

Keywords

  • Uncertainty in AI: Bayesian networks
  • Constraint Satisfaction and Optimization: Constraint satisfaction
  • Uncertainty in AI: Probabilistic programming
  • Uncertainty in AI: Inference
  • Constraint Satisfaction and Optimization: Solvers and tools

Abstract

Model counting of Disjunctive Normal Form (DNF) formulas is a critical problem in applications such as probabilistic inference and network reliability.

For example, it is often used for query evaluation in probabilistic databases. Due to the computational intractability of exact DNF counting, there has been a

line of research into a variety of approximation algorithms.

We develop a new Monte Carlo approach with an adaptive stopping rule and short-circuit formula evaluation. We prove it achieves Probably Approximately Correct (PAC) learning bounds and has

asymptotically improved time and randomness complexity compared to previous methods. We also show experimentally that it out-performs prior algorithms by at least three orders of magnitude in running time and scalability.

Paul Burkhardt, David G. Harris, and Kevin T. Schmitt. 2026. Scalable Algorithms for Approximate DNF Model Counting. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 2191–2198. https://doi.org/10.24963/ijcai.2026/244
Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards