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Found 4 papers, 1 papers with code
Graph Neural Networks for Reasoning 2-Quantified Boolean Formulas
In this paper, we target the QBF (Quantified Boolean Formula) satisfiability problem, the complexity of which is in-between propositional logic and predicate logic, and investigate the feasibility of learning GNN-based solvers and GNN-based heuristics for the cases with a universal-existential quantifier alternation (so-called 2QBF problems).
Graph Neural Reasoning May Fail in Certifying Boolean Unsatisfiability
It is feasible and practically-valuable to bridge the characteristics between graph neural networks (GNNs) and logical reasoning.
Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching
A broad range of cross-$m$-domain generation researches boil down to matching a joint distribution by deep generative models (DGMs).
Graph Neural Reasoning for 2-Quantified Boolean Formula Solvers
In this paper, we investigate the feasibility of learning GNN (Graph Neural Network) based solvers and GNN-based heuristics for specified QBF (Quantified Boolean Formula) problems.
