Summary of Learning Guided Automated Reasoning: a Brief Survey, by Lasse Blaauwbroek et al.
Learning Guided Automated Reasoning: A Brief Survey
by Lasse Blaauwbroek, David Cerna, Thibault Gauthier, Jan Jakubův, Cezary Kaliszyk, Martin Suda, Josef Urban
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Logic in Computer Science (cs.LO); Neural and Evolutionary Computing (cs.NE); Symbolic Computation (cs.SC)
GrooveSquid.com Paper Summaries
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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 explores how machine learning can be used to improve automated theorem provers and formal proof assistants. These general reasoning systems are capable of solving arbitrary mathematical problems, but their performance is influenced by various heuristics and choice points. Machine learning can guide these systems’ work, while deductive search allows training on large reasoning corpora. By combining correct-by-construction proofs with precise guidance, it’s possible to bootstrap into very large corpora with increasingly complex reasoning chains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Automated theorem provers and formal proof assistants are amazing tools that can solve really hard math problems. But they have a problem – they get overwhelmed by the huge number of possibilities. That’s where machine learning comes in. It can help these systems make better decisions and improve their performance. At the same time, these systems can be used to train machine learning models on large datasets. This creates a feedback loop that allows both sides to learn from each other. |
Keywords
* Artificial intelligence * Machine learning
