mlsanityData to 7 Oct 2026

The Model Counting Competition 2020

arXiv 2012.01323 ↗First posted 2 Dec 2020cs.LO cs.AI cs.DScited by 24

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Abstract (arXiv)

Many computational problems in modern society account to probabilistic reasoning, statistics, and combinatorics. A variety of these real-world questions can be solved by representing the question in (Boolean) formulas and associating the number of models of the formula directly with the answer to the question. Since there has been an increasing interest in practical problem solving for model counting over the last years, the Model Counting (MC) Competition was conceived in fall 2019. The competition aims to foster applications, identify new challenging benchmarks, and to promote new solvers and improve established solvers for the model counting problem and versions thereof. We hope that the results can be a good indicator of the current feasibility of model counting and spark many new applications. In this paper, we report on details of the Model Counting Competition 2020, about carrying out the competition, and the results. The competition encompassed three versions of the model counting problem, which we evaluated in separate tracks. The first track featured the model counting problem (MC), which asks for the number of models of a given Boolean formula. On the second track, we challenged developers to submit programs that solve the weighted model counting problem (WMC). The last track was dedicated to projected model counting (PMC). In total, we received a surprising number of 9 solvers in 34 versions from 8 groups.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Dec 2020.

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Citing papers per year: 2021: 5; 2022: 5; 2023: 3; 2024: 5; 2025: 5; 2026: 1.

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  1. 2021NeuroBack: Improving CDCL SAT Solving using Graph Neural Networkscited by 19
  2. 2021Rushing and Strolling among Answer Sets -- Navigation Made Easycited by 9
  3. 2024On the Hardness of Probabilistic Neurosymbolic Learningcited by 9
  4. 2021Partition Function Estimation: A Quantitative Studycited by 8
  5. 2022Treewidth-aware Reductions of Normal ASP to SAT -- Is Normal ASP Harder than SAT after All?cited by 8
  6. 2023Rounding Meets Approximate Model Countingcited by 6
  7. 2021Utilizing Treewidth for Quantitative Reasoning on Epistemic Logic Programscited by 3
  8. 2022DPMS: An ADD-Based Symbolic Approach for Generalized MaxSAT Solvingcited by 3
  9. 2021Projected Model Counting: Beyond Independent Supportcited by 2
  10. 2022A Quantitative Symbolic Approach to Individual Human Reasoningcited by 2
  11. 2023IASCAR: Incremental Answer Set Counting by Anytime Refinementcited by 2
  12. 2024Model Counting in the Wildcited by 2
12 more
  1. 2025Counting and Reasoning with Planscited by 2
  2. 2022Advanced Tools and Methods for Treewidth-Based Problem Solving -- Extended Abstractcited by 1
  3. 2023Solving Projected Model Counting by Utilizing Treewidth and its Limitscited by 1
  4. 2024Quantum Algorithms for Weighted Constrained Sampling and Weighted Model Countingcited by 1
  5. 2025The Gradient of Algebraic Model Countingcited by 1
  6. 2022Generation and Prediction of Difficult Model Counting Instancescited by 0
  7. 2024Extended Version of: On the Structural Hardness of Answer Set Programming: Can Structure Efficiently Confine the Power of Disjunctions?cited by 0
  8. 2024Techniques for Measuring the Inferential Strength of Forgetting Policiescited by 0
  9. 2025Solving Satisfiability Modulo Counting Exactly with Probabilistic Circuitscited by 0
  10. 2025Pseudo-Boolean d-DNNF Compilation for Expressive Feature Modeling Constructscited by 0
  11. 2025Numerical Considerations in Weighted Model Countingcited by 0
  12. 2026CSB: A Counting and Sampling tool for Bit-vectorscited by 0