Abstract

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.

BibTeX

@inproceedings{CKJUJV-PAAR14,
  author    = "Cezary Kaliszyk and Josef Urban and Ji\v{r}\'i Vysko\v{c}il",
  title     = "Machine Learner for Automated Reasoning 0.4 and 0.5",
  booktitle = "Proceedings of the 4th Workshop on Practical Aspects of
               Automated Reasoning (PAAR 2014)",
  editor    = "Stephan Schulz and Leonardo De Moura and Boris Konev",
  series    = "EasyChair Proceedings in Computer Science",
  volume    = 31,
  pages     = "60--66",
  year      = 2015
}