MatthiasNickles/diff-SAT

Probabilistic Answer Set Programming and Probabilistic SAT solving, based on Differentiable Satisfiability

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This tool helps researchers and practitioners in AI and logic programming who need to solve complex reasoning problems that involve both logical rules and probabilities. You provide logical rules and potentially fuzzy or probabilistic constraints (as cost functions), and it outputs a collection of models (like possible scenarios) that best fit those constraints, along with their associated probabilities. This is useful for those working with knowledge representation and combinatorial search.

No commits in the last 6 months.

Use this if you need to combine strict logical rules with probabilistic information or differentiable cost functions to find an optimal set of possible solutions, especially in domains like probabilistic logic programming or satisfiability.

Not ideal if your problem is purely deterministic with no probabilistic elements or if you only need a standard SAT or ASP solver without multi-model optimization or gradient-based reasoning.

Probabilistic Reasoning Logic Programming Knowledge Representation Combinatorial Optimization AI Inference
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 9 / 25
Maturity 16 / 25
Community 6 / 25

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Stars

70

Forks

3

Language

Scala

License

MIT

Last pushed

Jul 01, 2024

Commits (30d)

0

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