Currently, the non-transparent computing process of deep learning has become a significant
reason hindering its further development. The Neural-Symbolic (NS) system formed by …

State-of-the-art automated theorem provers explore large search spaces with carefully-
engineered routines, but most do not learn from past experience as human mathematicians …

Automated theorem provers have traditionally relied on manually tuned heuristics to guide
how they perform proof search. Deep reinforcement learning has been proposed as a way to …

Traditional automated theorem provers have relied on manually tuned heuristics to guide
how they perform proof search. Recently, however, there has been a surge of interest in the …

Theorem proving is a fundamental aspect of mathematics, spanning from informal reasoning
in mathematical language to rigorous derivations in formal systems. In recent years, the …

Premise selection is crucial for large theory reasoning with automated theorem provers as
the sheer size of the problems quickly leads to resource exhaustion. This paper proposes a …

Visual reasoning is essential for building intelligent agents that understand the world and
perform problem-solving beyond perception. Differentiable forward reasoning has been …

Automated theorem provers and formal proof assistants are general reasoning systems that
are in theory capable of proving arbitrarily hard theorems, thus solving arbitrary problems …

Using reinforcement learning for automated theorem proving has recently received much
attention. Current approaches use representations of logical statements that often rely on the …

The state-of-the-art superposition-based theorem provers for first-order logic rely on
simplification orderings on terms to constrain the applicability of inference rules, which in …