# Guiding Inferences in Connection Tableau by Recurrent Neural Networks

## TL;DR

This paper explores using recurrent neural networks to guide clause selection in connection tableau proofs, demonstrating their effectiveness and ability to generate new goals, with comparisons to gradient boosted trees.

## Contribution

It introduces a novel application of RNNs for guiding clause selection and goal conjecturing in connection tableau proof systems, with detailed training and experimental analysis.

## Key findings

- RNNs effectively guide clause selection in connection tableau proofs.
- The RNN-based approach outperforms gradient boosted trees in accuracy.
- The system can generate new tableau goals, demonstrating generative capabilities.

## Abstract

We present a dataset and experiments on applying recurrent neural networks (RNNs) for guiding clause selection in the connection tableau proof calculus. The RNN encodes a sequence of literals from the current branch of the partial proof tree to a hidden vector state; using it, the system selects a clause for extending the proof tree. The training data and learning setup are described, and the results are discussed and compared with state of the art using gradient boosted trees. Additionally, we perform a conjecturing experiment in which the RNN does not just select an existing clause, but completely constructs the next tableau goal.