PySAT: A Python Toolkit for Prototyping with SAT Oracles

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Optimal Decision Lists using SAT (2020, methodology): “Implementation All the models proposed in Section 4, and Section 5 are implemented as a set of Python scripts and solving is done by instrumenting calls to exact MaxSAT solver RC2-B (Ignatiev, Morgado, and Marques-Silva 2018, 2019).”
On Relating Explanations and Adversarial Examples (2019, methodology): “The implementation of minimum hitting set enumeration of Algorithm 1 is based on an award-winning maximum satisfiability solver RC26 [22] written on top of the PySAT toolkit [21].”
Abduction-Based Explanations for Machine Learning Models (2018, methodology): “…2017), but also efforts to explain predictions made by such models (Evans and Grefenstette 2018; Li et al. 2018; Ribeiro, Singh, and Guestrin 2018; Ignatiev et al. 2018; Montavon, Samek, and M ¨ uller 2018; Ribeiro, Singh, and Guestrin 2016; Lakkaraju, Bach, and Leskovec 2016; Baehrens et al.…”
SAT-Based Encodings for Optimal Decision Trees with Explicit Paths (2020, methodology): “These are also turned into CNF through standard means, the implementation avails of the cardinality encodings in the tool PySAT [18,26].”
RC2: an Efficient MaxSAT Solver (2019, neutral): “[11].”
ProcTHOR: Large-Scale Embodied AI Using Procedural Generation (2022, methodology): “10 [50] Alexey Ignatiev, Antonio Morgado, and Joao Marques-Silva.”