Abstract
In this paper, an overview of state-of-the-art techniques for premise selection in large theory mathematics is provided, and new premise selection techniques are introduced. Several evaluation metrics are introduced, compared and their appropriateness is discussed in the context of automated reasoning in large theory mathematics. The methods are evaluated on the MPTP2078 benchmark, a subset of the Mizar library, and a 10% improvement is obtained over the best method so far. © 2012 Springer-Verlag.
Cite
CITATION STYLE
Kühlwein, D., Van Laarhoven, T., Tsivtsivadze, E., Urban, J., & Heskes, T. (2012). Overview and evaluation of premise selection techniques for large theory mathematics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7364 LNAI, pp. 378–392). https://doi.org/10.1007/978-3-642-31365-3_30
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
