2,834 research outputs found
ENIGMA: Efficient Learning-based Inference Guiding Machine
ENIGMA is a learning-based method for guiding given clause selection in
saturation-based theorem provers. Clauses from many proof searches are
classified as positive and negative based on their participation in the proofs.
An efficient classification model is trained on this data, using fast
feature-based characterization of the clauses . The learned model is then
tightly linked with the core prover and used as a basis of a new parameterized
evaluation heuristic that provides fast ranking of all generated clauses. The
approach is evaluated on the E prover and the CASC 2016 AIM benchmark, showing
a large increase of E's performance.Comment: Submitted to LPAR 201
ProofWatch: Watchlist Guidance for Large Theories in E
Watchlist (also hint list) is a mechanism that allows related proofs to guide
a proof search for a new conjecture. This mechanism has been used with the
Otter and Prover9 theorem provers, both for interactive formalizations and for
human-assisted proving of open conjectures in small theories. In this work we
explore the use of watchlists in large theories coming from first-order
translations of large ITP libraries, aiming at improving hammer-style
automation by smarter internal guidance of the ATP systems. In particular, we
(i) design watchlist-based clause evaluation heuristics inside the E ATP
system, and (ii) develop new proof guiding algorithms that load many previous
proofs inside the ATP and focus the proof search using a dynamically updated
notion of proof matching. The methods are evaluated on a large set of problems
coming from the Mizar library, showing significant improvement of E's standard
portfolio of strategies, and also of the previous best set of strategies
invented for Mizar by evolutionary methods.Comment: 19 pages, 10 tables, submitted to ITP 2018 at FLO
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