NeuroBack: Improving CDCL SAT Solving using Graph Neural Networks

no code implementations • 26 Oct 2021 • Wenxi Wang, Yang Hu, Mohit Tiwari, Sarfraz Khurshid, Kenneth McMillan, Risto Miikkulainen

Aiming to make GNN improvements practical, this paper proposes an approach called NeuroBack, which builds on two insights: (1) predicting phases (i. e., values) of variables appearing in the majority (or even all) of the satisfying assignments are essential for CDCL SAT solving, and (2) it is sufficient to query the neural model only once for the predictions before the SAT solving starts.

A Survey of Hybrid Human-Artificial Intelligence for Social Computing

no code implementations • 17 Mar 2021 • Wenxi Wang, Huansheng Ning, Feifei Shi, Sahraoui Dhelim, Weishan Zhang, Liming Chen

In particular with the boom of artificial intelligence (AI), social computing is significantly influenced by AI.

Unity

A Study of the Learnability of Relational Properties: Model Counting Meets Machine Learning (MCML)

no code implementations • 25 Dec 2019 • Muhammad Usman, Wenxi Wang, Kaiyuan Wang, Marko Vasic, Haris Vikalo, Sarfraz Khurshid

However, MCML metrics based on model counting show that the performance can degrade substantially when tested against the entire (bounded) input space, indicating the high complexity of precisely learning these properties, and the usefulness of model counting in quantifying the true performance.

BIG-bench Machine Learning