Automated Theorem Proving
70 papers with code • 10 benchmarks • 8 datasets
The goal of Automated Theorem Proving is to automatically generate a proof, given a conjecture (the target theorem) and a knowledge base of known facts, all expressed in a formal language. Automated Theorem Proving is useful in a wide range of applications, including the verification and synthesis of software and hardware systems.
Source: Learning to Prove Theorems by Learning to Generate Theorems
Benchmarks
These leaderboards are used to track progress in Automated Theorem Proving
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Libraries
Use these libraries to find Automated Theorem Proving models and implementations
MiniF2F
MED
Geometry3K
Kinship
HOList
ProofNet
Most implemented papers
Learning Maximally Monotone Operators for Image Recovery
basp-group/PnP-MMO-imaging
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Recently, several works have proposed to replace the operator related to the regularization by a more sophisticated denoiser.
Learning to Match Mathematical Statements with Proofs
mcoavoux/statement_proof_matching
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The task is designed to improve the processing of research-level mathematical texts.
Learning Symbolic Rules for Reasoning in Quasi-Natural Language
In this work, we ask how we can build a rule-based system that can reason with natural language input but without the manual construction of rules.
ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics
We introduce ProofNet, a benchmark for autoformalization and formal proving of undergraduate-level mathematics.
Lectures on Jacques Herbrand as a Logician
We give some lectures on the work on formal logic of Jacques Herbrand, and sketch his life and his influence on automated theorem proving.
HOL(y)Hammer: Online ATP Service for HOL Light
01mf02/notes
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HOL(y)Hammer is an online AI/ATP service for formal (computer-understandable) mathematics encoded in the HOL Light system.
HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving
We propose various machine learning tasks that can be performed on this dataset, and discuss their significance for theorem proving.
On-demand Injection of Lexical Knowledge for Recognising Textual Entailment
We approach the recognition of textual entailment using logical semantic representations and a theorem prover.
LangPro: Natural Language Theorem Prover
LangPro is an automated theorem prover for natural language (https://github. com/kovvalsky/LangPro).
