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Found 10 papers, 4 papers with code
Graph2Tac: Learning Hierarchical Representations of Math Concepts in Theorem proving
G2T is an online model that is deeply integrated into the users' workflow and can adapt in real time to new Coq projects and their definitions.
Machine Learning Meets The Herbrand Universe
In particular, we develop a GNN2RNN architecture based on an invariant graph neural network (GNN) that learns from problems and their solutions independently of symbol names (addressing the abundance of skolems), combined with a recurrent neural network (RNN) that proposes for each clause its instantiations.
The Isabelle ENIGMA
We significantly improve the performance of the E automated theorem prover on the Isabelle Sledgehammer problems by combining learning and theorem proving in several ways.
Learning Theorem Proving Components
Saturation-style automated theorem provers (ATPs) based on the given clause procedure are today the strongest general reasoners for classical first-order logic.
Fast and Slow Enigmas and Parental Guidance
The second addition is motivated by fast weight-based rejection filters that are currently used in systems like E and Prover9.
The Role of Entropy in Guiding a Connection Prover
This leads us to explore how the entropy of the inference selection implemented via the neural network influences the proof search.
ω-categorical structures avoiding height 1 identities
The algebraic dichotomy conjecture for Constraint Satisfaction Problems (CSPs) of reducts of (infinite) finitely bounded homogeneous structures states that such CSPs are polynomial-time tractable if the model-complete core of the template has a pseudo-Siggers polymorphism, and NP-complete otherwise.
Logic Logic in Computer Science Rings and Algebras
ENIGMA Anonymous: Symbol-Independent Inference Guiding Machine (system description)
For the neural guidance, we use symbol-independent graph neural networks (GNNs) and their embedding of the terms and clauses.
Property Invariant Embedding for Automated Reasoning
This encoding represents symbols only by nodes in the graph, without giving the network any knowledge of the original labels.
