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

We present an environment, benchmark, and deep learning driven automated theorem prover for higher-order logic.

Ranked #2 on Automated Theorem Proving on HOList benchmark

We propose various machine learning tasks that can be performed on this dataset, and discuss their significance for theorem proving.

Ranked #3 on Automated Theorem Proving on HolStep (Unconditional)

Proof assistants offer a formalism that resembles human mathematical reasoning, representing theorems in higher-order logic and proofs as high-level tactics.

Ranked #1 on Automated Theorem Proving on CoqGym

We approach the recognition of textual entailment using logical semantic representations and a theorem prover.

AUTOMATED THEOREM PROVING INFORMATION RETRIEVAL NATURAL LANGUAGE INFERENCE QUESTION ANSWERING

Tackling Natural Language Inference with a logic-based method is becoming less and less common.

LangPro is an automated theorem prover for natural language (https://github. com/kovvalsky/LangPro).

In this paper, we introduce a system called GamePad that can be used to explore the application of machine learning methods to theorem proving in the Coq proof assistant.

The Boolean Satisfiability (SAT) problem is the canonical NP-complete problem and is fundamental to computer science, with a wide array of applications in planning, verification, and theorem proving.

I propose a system for Automated Theorem Proving in higher order logic using deep learning and eschewing hand-constructed features.

Ranked #3 on Automated Theorem Proving on Metamath set.mm