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- W4387356306 abstract "Bridging logical reasoning and deep learning is crucial for advanced AI systems. In this work, we present a new framework that addresses this goal by generating interpretable and verifiable logical rules through differentiable learning, without relying on pre-specified logical structures. Our approach builds upon SATNet, a differentiable MaxSAT solver that learns the underlying rules from input-output examples. Despite its efficacy, the learned weights in SATNet are not straightforwardly interpretable, failing to produce human-readable rules. To address this, we propose a novel specification method called maximum equality, which enables the interchangeability between the learned weights of SATNet and a set of propositional logical rules in weighted MaxSAT form. With the decoded weighted MaxSAT formula, we further introduce several effective verification techniques to validate it against the ground truth rules. Experiments on stream transformations and Sudoku problems show that our decoded rules are highly reliable: using exact solvers on them could achieve 100% accuracy, whereas the original SATNet fails to give correct solutions in many cases. Furthermore, we formally verify that our decoded logical rules are functionally equivalent to the ground truth ones." @default.
- W4387356306 created "2023-10-05" @default.
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- W4387356306 date "2023-10-03" @default.
- W4387356306 modified "2023-10-06" @default.
- W4387356306 title "Learning Reliable Logical Rules with SATNet" @default.
- W4387356306 doi "https://doi.org/10.48550/arxiv.2310.02133" @default.
- W4387356306 hasPublicationYear "2023" @default.
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