Competition

The 5th Competition on Model Counting (MC 2024) is a competition to deepen the relationship between latest theoretical and practical development on the various model counting problems and their practical applications. It targets the problem of counting the number of models of a Boolean formula.

Further details on the competition can be found at: Competition 2024

Workshop on Counting, Sampling, and Synthesis

The Workshop on Counting, Sampling, and Synthesis is an event for researchers in model counting and sampling. It covers advanced topics such as weighted and projected counters/samplers and various domains such as SAT, SMT, ASP, and CP. This year, the workshop has expanded its focus to include the role of model counters, samplers, and solvers in automated synthesis. The goal of the workshop is to facilitate the exchange of cutting-edge theoretical and practical insights, with a particular emphasis on innovative solver technologies and their real-world applications. Additionally, the workshop provides an opportunity for developers of model counters to showcase their work and share detailed competition results, to encourage discussions that bridge theory and practice.

This year’s workshop will be organized as part of the SAT 2024 Conference. For more details, please refer to Workshop 2024.

References

When refering to the competition in an academic paper please use the following reference:

@article{10.1145/3459080,
	address = {New York, NY, USA},
	articleno = {13},
	author = {Fichte, Johannes K. and Hecher, Markus and Hamiti, Florim},
	doi = {10.1145/3459080},
	issn = {1084-6654},
	issue_date = {December 2021},
	journal = {ACM J. Exp. Algorithmics},
	month = {oct},
	numpages = {26},
	publisher = {Association for Computing Machinery},
	title = {The Model Counting Competition 2020},
	volume = {26},
	year = {2021}}

Sponsors

• The organizers gratefully acknowledge the computing time made available to them on the high-performance computer at the NHR Center of TU Dresden. This center is jointly supported by the Federal Ministry of Education and Research and the state governments participating in the NHR (www.nhr-verein.de/unsere-partner).
• Computations were partially enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS), partially funded by the Swedish Research Council through grant agreement no. 2022-06725.