TY - JOUR AU - Abboud, Ralph AU - Ceylan, Ismail AU - Lukasiewicz, Thomas PY - 2020/04/03 Y2 - 2024/03/28 TI - Learning to Reason: Leveraging Neural Networks for Approximate DNF Counting JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 04 SE - AAAI Technical Track: Machine Learning DO - 10.1609/aaai.v34i04.5705 UR - https://ojs.aaai.org/index.php/AAAI/article/view/5705 SP - 3097-3104 AB - <p>Weighted model counting (WMC) has emerged as a prevalent approach for probabilistic inference. In its most general form, WMC is #P-hard. Weighted DNF counting (weighted #DNF) is a special case, where approximations with probabilistic guarantees are obtained in <em>O</em>(<em>nm</em>), where <em>n</em> denotes the number of variables, and <em>m</em> the number of clauses of the input DNF, but this is not scalable in practice. In this paper, we propose a <em>neural model counting</em> approach for weighted #DNF that combines approximate model counting with deep learning, and accurately approximates model counts in linear time when width is bounded. We conduct experiments to validate our method, and show that our model learns and generalizes very well to large-scale #DNF instances.</p> ER -