Learning Heuristics for Quantified Boolean Formulas through Reinforcement Learning
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| Authors | Edward A. Lee, Gil Lederman, Markus Rabe, Sanjit Seshia |
| Journal/Conference Name | ICLR 2020 1 |
| Paper Category | Artificial Intelligence |
| Paper 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. |
| Date of publication | 2020 |
| Code Programming Language | Unspecified |
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