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Theoretical Foundation of Solvers: Context, Directions and Open Problems
Vijay Ganesh (University of Waterloo), Laurent Simon (Bordeaux INP), and David …
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Structure of SAT Instances
Jordi Levy (Artificial Intelligence Research Institute, Spanish National Resear…
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Theory of Encodings
Oliver Kullmann (Swansea University), Ciaran McCreesh (University of Glasgow), …
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Encodings Showcase
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Proof Complexity Toolbox
Robert Robere (McGill University), Susanna de Rezende (Czech Academy of Science…
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The Proof Complexity of Solvers
Paul Beame, Marc Vinyals, Noah Fleming, Chunxiao (Ian) Li
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Non-CDCL Solvers
Marijn Heule (Carnegie Mellon University), Jakob Nordstrom (University of Copenhagen & Lund University), Zhiwei Zhang (Rice University)
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Theorem Proving Strategies and Decision Procedures for Satisfiability
Maria Paola Bonacina (Università degli Studi di Verona), Philipp Rümmer (Uppsala University)
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Finite Model Theory
Albert Atserias (UPC Barcelona), Benedikt Pago (Rwth Aachen University), Antonina Kolokolova (Memorial University of Newfoundland)
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SAT and Foundations of Mathematics
Sasha Razborov (University of Chicago), Pavel Pudlák (Czech Academy of Sciences), Shai Ben-David (University of Waterloo)
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SAT for Math
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Computer Algebra and SAT for Mathematical Search
Curtis Bright (University of Windsor)
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Symbolic Computation Techniques in SMT Solving: Mathematical Beauty Meets Efficient Heuristics
Erika Abraham (RWTH Aachen University)
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An Automated Approach to the Collatz Conjecture
Emre Yolcu (Carnegie Mellon University)
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ML for Solvers
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Predicting Satisfiability at the Phase Transition via End-to-End Learning
Kevin Leyton Brown (University of British Columbia)
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Learning to Schedule Heuristics in Branch and Bound
Elias Khalil (University of Toronto)
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Extensions of CDCL Branching Heuristics by Exploration during Conflict Depression
Md. Solimul Chowdhury (University of Alberta)
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Learning to Solve SMT Formulas
Mislav Balunović (ETH Zurich)
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Preprocessing SAT, MaxSAT, and QBF
Benjamin Kiesl (SAP), Jeremias Berg (University of Helsinki), and Martina Seidl…
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Logic + Machine Learning
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Unifying Logical and Statistical AI with Markov Logic
Pedro Domingos (University of Washington)
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Constrained Gradient Descent Algorithm for Testing Neural Networks
Vineel Nagisetty (University of Waterloo)
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DL2: Training and Querying Neural Networks with Logic
Marc Fischer (ETH Zurich)
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Training Neural Networks with a Little Help from Knowledge
Vivek Srikumar (University of Utah)