Projected Model Counting
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Abstract (arXiv)
Model counting is the task of computing the number of assignments to variables V that satisfy a given propositional theory F. Model counting is an essential tool in probabilistic reasoning. In this paper, we introduce the problem of model counting projected on a subset P of original variables that we call 'priority' variables. The task is to compute the number of assignments to P such that there exists an extension to 'non-priority' variables VP that satisfies F. Projected model counting arises when some parts of the model are irrelevant to the counts, in particular when we require additional variables to model the problem we are counting in SAT. We discuss three different approaches to projected model counting (two of which are novel), and compare their performance on different benchmark problems. To appear in 18th International Conference on Theory and Applications of Satisfiability Testing, September 24-27, 2015, Austin, Texas, USA
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jul 2015.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 1; 2025: 2.
6 papers.
- 2023Engineering an Exact Pseudo-Boolean Model Countercited by 2
- 2023Solving Projected Model Counting by Utilizing Treewidth and its Limitscited by 1
- 2024Dynamic Blocked Clause Elimination for Projected Model Countingcited by 1
- 2021QuantifyML: How Good is my Machine Learning Model?cited by 0
- 2025Entailment vs. Verification for Partial-assignment Satisfiability and Enumerationcited by 0
- 2025Counting Answer Sets of Disjunctive Answer Set Programscited by 0
- 2025Counting Answer Sets of Disjunctive Answer Set Programs15 Jul 2025
- 2025Entailment vs. Verification for Partial-assignment Satisfiability and Enumeration3 Mar 2025
- 2024Dynamic Blocked Clause Elimination for Projected Model Counting12 Aug 2024
- 2023Engineering an Exact Pseudo-Boolean Model Counter19 Dec 2023
- 2023Solving Projected Model Counting by Utilizing Treewidth and its Limits30 May 2023
- 2021QuantifyML: How Good is my Machine Learning Model?25 Oct 2021