Summary of Grass: Combining Graph Neural Networks with Expert Knowledge For Sat Solver Selection, by Zhanguang Zhang et al.
GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver Selection
by Zhanguang Zhang, Didier Chetelat, Joseph Cotnareanu, Amur Ghose, Wenyi Xiao, Hui-Ling Zhen, Yingxue Zhang, Jianye Hao, Mark Coates, Mingxuan Yuan
First submitted to arxiv on: 17 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed GraSS approach employs a heterogeneous graph neural network (GNN) model to predict which SAT solver to select for a given instance. The method utilizes tripartite graph representations of instances, enriched with domain-specific knowledge such as novel node features and positional encodings for clauses. This combination enables improvements in runtime for a pool of seven state-of-the-art solvers on both industrial circuit design benchmarks and instances from the 2022 SAT Competition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GraSS is a new way to choose which computer program (SAT solver) to use to solve a complex problem. It uses special graphs and a type of artificial intelligence called a graph neural network. This helps pick the best solver for a specific problem, making it faster and more efficient. The approach was tested on real-world problems and showed improved results. |
Keywords
» Artificial intelligence » Gnn » Graph neural network
