Improving ENIGMA-style Clause Selection while Learning From History [chapter]

Martin Suda
2021 Lecture Notes in Computer Science  
AbstractWe 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
more » ... k 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.
doi:10.1007/978-3-030-79876-5_31 fatcat:t75bhcntqnao7nt4g4drb4qq5m