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- W3135404769 abstract "We re-examine the topic of machine-learned clause selection guidance in saturation-based theorem provers. The central idea, recently popularized by the ENIGMA system, is to learn a classifier for recognizing clauses that appeared in previously discovered proofs. In subsequent runs, clauses classified positively are prioritized for selection. We propose several improvements to this approach and experimentally confirm their viability. For the demonstration, we use a Recursive Neural Network to classify clauses based on their derivation history and the presence or absence of automatically supplied theory axioms therein. The automatic theorem prover Vampire guided by the network achieves a 41% improvement on a relevant subset of SMT-LIB in a real time evaluation." @default.
- W3135404769 created "2021-03-15" @default.
- W3135404769 creator A5065681566 @default.
- W3135404769 date "2021-02-26" @default.
- W3135404769 modified "2023-09-27" @default.
- W3135404769 title "New Techniques that Improve ENIGMA-style Clause Selection Guidance." @default.
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