HOL Light QE

Author(s):  
Jacques Carette ◽  
William M. Farmer ◽  
Patrick Laskowski
Keyword(s):  
2018 ◽  
Vol 63 (3) ◽  
pp. 787-808
Author(s):  
Li-Ming Li ◽  
Zhi-Ping Shi ◽  
Yong Guan ◽  
Qian-Ying Zhang ◽  
Yong-Dong Li
Keyword(s):  

10.29007/5gzr ◽  
2018 ◽  
Author(s):  
Cezary Kaliszyk ◽  
Josef Urban

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%.


2006 ◽  
Vol 144 (2) ◽  
pp. 43-51 ◽  
Author(s):  
Sean McLaughlin ◽  
Clark Barrett ◽  
Yeting Ge
Keyword(s):  

2012 ◽  
Vol 50 (2) ◽  
pp. 173-190 ◽  
Author(s):  
John Harrison

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