ICLR 2020poster41 citations
Learning Heuristics for Quantified Boolean Formulas through Reinforcement Learning
Gil Lederman, Markus Rabe, Sanjit Seshia, Edward A. Lee
Abstract
We demonstrate how to learn efficient heuristics for automated reasoning algorithms for quantified Boolean formulas through deep reinforcement learning. We focus on a backtracking search algorithm, which can already solve formulas of impressive size - up to hundreds of thousands of variables. The main challenge is to find a representation of these formulas that lends itself to making predictions in a scalable way. For a family of challenging problems, we learned a heuristic that solves significantly more formulas compared to the existing handwritten heuristics.
LogicQBFLogical ReasoningSATGraphReinforcement LearningGNN
BibTeX
@inproceedings{
Lederman2020Learning,
title={Learning Heuristics for Quantified Boolean Formulas through Reinforcement Learning},
author={Gil Lederman and Markus Rabe and Sanjit Seshia and Edward A. Lee},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=BJluxREKDB}
}