Automata learning is a popular technique used to automatically construct an automaton model from queries. Much research went into devising ad hoc adaptations of algorithms for different types of automata. The CALF project seeks to unify these using category theory in order to ease correctness proofs and guide the design of new algorithms. In this paper, we extend CALF to cover learning of algebraic structures that may not have a coalgebraic presentation. Furthermore, we provide a detailed algorithmic account of an abstract version of the popular L* algorithm, which was missing from CALF. We instantiate the abstract theory to a large class of Set functors, by which we recover for the first time practical tree automata learning algorithms from an abstract framework and at the same time obtain new algorithms to learn algebras of quotiented polynomial functors.

中文翻译：

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学习广义树自动机的分类框架

自动机学习是一种流行的技术，用于根据查询自动构建自动机模型。许多研究都涉及为不同类型的自动机设计算法的临时改编。CALF 项目试图使用类别理论来统一这些，以简化正确性证明并指导新算法的设计。在本文中，我们扩展 CALF 以涵盖可能没有代数表示的代数结构的学习。此外，我们提供了 CALF 中缺少的流行 L* 算法的抽象版本的详细算法说明。我们将抽象理论实例化为一大类集合函子，