# Learning Equational Theorem Proving

@article{Piepenbrock2021LearningET, title={Learning Equational Theorem Proving}, author={Jelle Piepenbrock and Tom M. Heskes and Mikol'avs Janota and Josef Urban}, journal={ArXiv}, year={2021}, volume={abs/2102.05547} }

We develop Stratified Shortest Solution Imitation Learning (3SIL) to learn equational theorem proving in a deep reinforcement learning (RL) setting. The self-trained models achieve state-of-the-art performance in proving problems generated by one of the top open conjectures in quasigroup theory, the Abelian Inner Mapping (AIM) Conjecture. To develop the methods, we first use two simpler arithmetic rewriting tasks that share tree-structured proof states and sparse rewards with the AIM problems…

## 2 Citations

### Similarity-Based Equational Inference in Physics

- MathematicsPhysical Review Research
- 2021

A symbolic similarity-based heuristic search is implemented to solve the equation reconstruction task as an early step towards multi-hop equational inference in physics.

### PhysNLU: A Language Resource for Evaluating Natural Language Understanding and Explanation Coherence in Physics

- Computer ScienceLREC
- 2022

A collection of datasets developed to evaluate the performance of language models in this regard are presented, which measure capabilities with respect to sentence ordering, position, section prediction, and discourse coherence.

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