Learned Provability Likelihood for Tactical Search
@article{Gauthier2021LearnedPL, title={Learned Provability Likelihood for Tactical Search}, author={Thibault Gauthier}, journal={ArXiv}, year={2021}, volume={abs/2109.03234} }
We present a method to estimate the provability of a mathematical formula. We adapt the tactical theorem prover TacticToe to factor in these estimations. Experiments over the HOL4 library show an increase in the number of theorems re-proven by TacticToe thanks to this additional guidance. This amelioration in performance together with concurrent updates to the TacticToe framework lead to an improved user experience.
References
SHOWING 1-10 OF 18 REFERENCES
TacticToe: Learning to Prove with Tactics
- Computer ScienceJournal of Automated Reasoning
- 2020
An automated tactical prover TacticToe is implemented on top of the HOL4 interactive theorem prover, which learns from human proofs which mathematical technique is suitable in each proof situation and is used in a Monte Carlo tree search algorithm to explore promising tactic-level proof paths.
Reinforcement Learning of Theorem Proving
- Computer ScienceNeurIPS
- 2018
A theorem proving algorithm that uses practically no domain heuristics for guiding its connection-style proof search and solves within the same number of inferences over 40% more problems than a baseline prover, which is an unusually high improvement in this hard AI domain.
The Tactician - A Seamless, Interactive Tactic Learner and Prover for Coq
- Computer ScienceCICM
- 2020
An overview of Tactician from the user's point of view, regarding both day-to-day usage and issues of package dependency management while learning in the large and a peek into the implementation as a Coq plugin and machine learning platform.
PaMpeR: Proof Method Recommendation System for Isabelle/HOL
- Computer Science2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE)
- 2018
PaMpeR, a proof method recommendation system for Isabelle/HOL, correctly predicts experienced users' proof methods invocation, especially when it comes to special purpose proof methods.
HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving
- Computer ScienceICML
- 2019
Continuous Upper Confidence Trees with Polynomial Exploration - Consistency
- Computer Science, MathematicsECML/PKDD
- 2013
A proof in the case of fully observable Markov Decision Processes with bounded horizon, possibly including infinitely many states, infinite action space and arbitrary stochastic transition kernels is proposed.
Tree Neural Networks in HOL4
- Computer ScienceCICM
- 2020
An implementation of tree neural networks within the proof assistant HOL4 is presented, which makes them naturally suited for approximating functions whose domain is a set of formulas.
A Brief Overview of HOL4
- Computer ScienceTPHOLs
- 2008
The HOLF proof assistant supports specification and proof in classical higher order logic and how it may be applied in formal verification is given.
HOL Light: An Overview
- Computer ScienceTPHOLs
- 2009
HOL Light is an interactive proof assistant for classical higher-order logic intended as a clean and simplified version of Mike Gordon's original HOL system and provides powerful proof tools and has been applied to some non-trivial tasks in the formalization of mathematics and industrial formal verification.
ENIGMA-NG: Efficient Neural and Gradient-Boosted Inference Guidance for E
- Computer ScienceCADE
- 2019
The resulting methods improve on the manually designed clause guidance, providing the first practically convincing application of gradient-boosted and neural clause guidance in saturation-style automated theorem provers.