Saturation-style automated theorem provers (ATPs) based on the given clause procedure are today the strongest general reasoners for classical first-order logic. The clause selection heuristics in such systems are, however, often evaluating clauses in isolation, ignoring other clauses. This has changed recently by equipping the E/ENIGMA system with a graph neural network (GNN) that chooses the next given clause based on its evaluation in the context of previously selected clauses. In this work, we describe several algorithms and experiments with ENIGMA, advancing the idea of contextual evaluation based on learning important components of the graph of clauses.

中文翻译：

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学习定理证明组件

基于给定子句过程的饱和式自动定理证明器 (ATP) 是当今最强大的经典一阶逻辑推理器。然而，此类系统中的子句选择启发式通常是孤立地评估子句，而忽略其他子句。最近通过为 E/ENIGMA 系统配备图神经网络 (GNN) 来改变这种情况，该网络根据在先前选择的子句的上下文中的评估来选择下一个给定的子句。在这项工作中，我们描述了几种使用 ENIGMA 的算法和实验，推进了基于学习从句图的重要组成部分的上下文评估的想法。