Machine Learning for Automated Theorem Proving
Sean B. Holden
Broschiertes Buch

Machine Learning for Automated Theorem Proving

Learning to Solve SAT and QSAT

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Automated theorem proving represents a significant and long-standing area of research in computer science, with numerous applications. A large proportion of the methods developed to date for the implementation of automated theorem provers (ATPs) have been algorithmic, sharing a great deal in common with the wider study of heuristic search algorithms. However, in recent years researchers have begun to incorporate machine learning (ML) methods into ATPs in an effort to extract better performance. Propositional satisfiability (SAT) solving and machine learning are both large and longstanding area...