Axiom selection over large theory based on new first-order formula metrics

Axiom selection is a task that selects the most likely useful axioms from a large-scale axiom set for proving a given conjecture. Existing axiom selection methods either solely take shallow symbols into account or strongly dependent on previous successful proofs from homologous problems. To address these problems, we introduce a new metric to evaluate the dissimilarity between formulae and utilize it as an evaluator in the selection task. Firstly, we propose a substitution-based metric to compute the dissimilarity between terms. It is a pseudo-metric and can capture the in-depth syntactic difference trigged by both functional and variable subterms. We then extend it to atoms and prove the atom metric also to be a pseudo-metric. Treating formulae as atom sets, we define three kinds of dissimilarity metrics between formulae. Finally, we design and implement conjecture-oriented axiom selection methods based on newly proposed formula metrics. The experimental evaluation is conducted on the MPTP2078 benchmark and demonstrates dissimilarity-based axiom selection improves E prover’s performance. In the best case, it increases the ratio of successful proofs from 30.90% to 42.25%.