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Stronger Automation for Flyspeck by Feature Weighting and Strategy Evolution

9 pagesPublished: May 26, 2013

Abstract

Two complementary AI methods are used to improve the strength of the AI/ATP service for proving conjectures over the HOL Light and Flyspeck corpora. First, several schemes for frequency-based feature weighting are explored in combination with distance-weighted k-nearest-neighbor classifier. This results in 16% improvement (39.0% to 45.5% Flyspeck problems solved) of the overall strength of the service when using 14 CPUs and 30 seconds. The best premise-selection/ATP combination is improved from 24.2% to 31.4%, i.e. by 30%. A smaller improvement is obtained by evolving targetted E prover strategies on two particular premise selections, using the Blind Strategymaker (BliStr) system. This raises the performance of the best AI/ATP method from 31.4% to 34.9%, i.e. by 11%, and raises the current 14-CPU power of the service to 46.9%.

Keyphrases: automatic theorem provers, feature weighting, Flyspeck, higher-order logic, HOL Light, large theories, machine learning, Strategy evolution

In: Jasmin Christian Blanchette and Josef Urban (editors). PxTP 2013. Third International Workshop on Proof Exchange for Theorem Proving, vol 14, pages 87--95

Links:
BibTeX entry
@inproceedings{PxTP2013:Stronger_Automation_for_Flyspeck,
  author    = {Cezary Kaliszyk and Josef Urban},
  title     = {Stronger Automation for Flyspeck by Feature Weighting and Strategy Evolution},
  booktitle = {PxTP 2013. Third International Workshop on Proof Exchange for Theorem Proving},
  editor    = {Jasmin Christian Blanchette and Josef Urban},
  series    = {EPiC Series in Computing},
  volume    = {14},
  pages     = {87--95},
  year      = {2013},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/VZv6},
  doi       = {10.29007/5gzr}}

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