Summary of Learning Better Representations From Less Data For Propositional Satisfiability, by Mohamed Ghanem et al.
Learning Better Representations From Less Data For Propositional Satisfiability
by Mohamed Ghanem, Frederik Schmitt, Julian Siber, Bernd Finkbeiner
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Logic in Computer Science (cs.LO)
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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 This paper presents a novel approach, called NeuRes, to tackle propositional satisfiability (a quintessential NP-complete problem) by combining certificate-driven training and expert iteration. The model learns better representations with much higher data efficiency, requiring orders of magnitude less training data than traditional methods. NeuRes employs propositional resolution as a proof system to generate proofs of unsatisfiability and accelerate truth assignment exploration. The architecture uses attention-based mechanisms to autoregressively select clauses from dynamic formula embeddings, while expert iteration replaces longer teacher proofs with model-generated ones. This self-improving workflow enables NeuRes to outperform NeuroSAT in correctly classified and proven instances. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to solve a very hard problem called propositional satisfiability. It’s like trying to find the right combination of lights on a big light switch panel. The traditional approach needs lots of data and takes a long time, but this new method uses a special kind of learning that can find the correct solution much faster. The model combines two techniques: one helps it learn from its mistakes, and the other makes sure it’s finding the right answer. This combination lets the model solve problems much more efficiently than before, even outperforming a specialized solver called NeuroSAT. |
Keywords
* Artificial intelligence * Attention
