{"type":"ScholarlyArticle","title":"Scalable Algorithms for Approximate DNF Model Counting","authors":[{"name":"Paul Burkhardt","slug":"paul-burkhardt","given":"Paul","family":"Burkhardt","affiliation":""},{"name":"David G. Harris","slug":"david-g-harris","given":"David G.","family":"Harris","affiliation":""},{"name":"Kevin T. Schmitt","slug":"kevin-t-schmitt","given":"Kevin T.","family":"Schmitt","affiliation":""}],"abstract":"Model counting of Disjunctive Normal Form (DNF) formulas is a critical problem in applications such as probabilistic inference and network reliability. \r\nFor example, it is often used for query evaluation in probabilistic databases. Due to the computational intractability of exact DNF counting, there has been a \r\nline of research into a variety of approximation algorithms.\r\n\r\nWe 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 \r\nasymptotically 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.","doi":"10.24963/ijcai.2026/244","url":"https://ijcai.org/proceedings/2026/244","pdf":"https://ijcai.org/proceedings/2026/0244.pdf","markdown":"https://ijcai.org/proceedings/2026/244.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/244","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Constraint Satisfaction and Optimization","track":"Main Track","number":244,"pages":{"first":2191,"last":2198},"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"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}