DeepAI
Log In Sign Up

Learning to Prove with Tactics

04/02/2018
by   Thibault Gauthier, et al.
0

We implement a automated tactical prover TacticToe on top of the HOL4 interactive theorem prover. TacticToe learns from human proofs which mathematical technique is suitable in each proof situation. This knowledge is then used in a Monte Carlo tree search algorithm to explore promising tactic-level proof paths. On a single CPU, with a time limit of 60 seconds, TacticToe proves 66.4 percent of the 7164 theorems in HOL4's standard library, whereas E prover with auto-schedule solves 34.5 percent. The success rate rises to 69.0 percent by combining the results of TacticToe and E prover.

READ FULL TEXT

page 1

page 2

page 3

page 4

04/02/2018

Learning to Reason with HOL4 tactics

Techniques combining machine learning with translation to automated reas...
11/18/2016

Monte Carlo Connection Prover

Monte Carlo Tree Search (MCTS) is a technique to guide search in a large...
05/23/2022

HyperTree Proof Search for Neural Theorem Proving

We propose an online training procedure for a transformer-based automate...
11/29/2012

Learning-Assisted Automated Reasoning with Flyspeck

The considerable mathematical knowledge encoded by the Flyspeck project ...
05/21/2019

Learning to Prove Theorems via Interacting with Proof Assistants

Humans prove theorems by relying on substantial high-level reasoning and...
09/03/2011

Eliciting implicit assumptions of proofs in the MIZAR Mathematical Library by property omission

When formalizing proofs with interactive theorem provers, it often happe...