Machine Learner for Automated Reasoning 0.4 and 0.5
This addresses the challenge of scaling automated reasoning for formal verification in mathematics and computer science, though it appears incremental as it builds on previous versions.
The paper tackles the problem of automated theorem proving in large formal libraries by developing MaLARea, a learning and reasoning system that leverages thousands of available theorems and related proofs to learn theorem-proving knowledge. The result is that the latest version of MaLARea won the 2013 CASC LTB competition by a large margin.
Machine Learner for Automated Reasoning (MaLARea) is a learning and reasoning system for proving in large formal libraries where thousands of theorems are available when attacking a new conjecture, and a large number of related problems and proofs can be used to learn specific theorem-proving knowledge. The last version of the system has by a large margin won the 2013 CASC LTB competition. This paper describes the motivation behind the methods used in MaLARea, discusses the general approach and the issues arising in evaluation of such system, and describes the Mizar@Turing100 and CASC'24 versions of MaLARea.