Abstract
This paper describes a system combining model-based and learning-based methods for automated reasoning in large theories, i.e. oil a large number of problems that use many axioms, lemmas, theorems, definitions, and symbols, in a consistent fashion. The implementation is based oil the existing MaLARea system, which cycles between theorem proving attempts and learning axiom relevance from successes.
This system is extended by taking into account semantic relevance of axioms, in a way similar to that of the SRASS system. The resulting combined system significantly outperforms both MaLARea and SRASS on the MPTP Challenge large theory benchmark, in terms of both the number of problems solved and the time taken to find solutions.
The design, implementation, and experimental testing of the system are described here.