Octopus: Combining Learning and Parallel Search

  • Authors:

  • Monty Newborn;Zongyan Wang

  • Affiliations:

  • School of Computer Science, McGill University, Canada;School of Computer Science, McGill University, Canada

  • Venue:
  • Journal of Automated Reasoning
  • Year:
  • 2004

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Abstract

This paper presents Octopus, an automated theorem-proving system that combines learning and parallel search. The learning technique involves proving a simpler version of a given theorem and then using what it has learned to prove the given theorem. As of January 2004 Octopus had successfully proved 43 of the 1.0-rated theorems of the TPTP Problem Library.