广义树自动机的分类学习框架
A Categorical Framework for Learning Generalised Tree Automata
摘要：
自动机学习是一种流行的技术，用于从查询中自动构造自动机模型。许多研究都致力于为不同类型的自动机设计专门的算法适应性。CALF项目试图使用范畴理论来统一这些，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涵盖学习的代数结构，可能没有一个coalgebraic介绍。此外，我们提供了一个详细的算法帐户的抽象版本的流行的L * 算法，这是从CALF失踪。我们将抽象理论实例化到一大类集合函子上，从而第一次从抽象框架中恢复出实用的树自动机学习算法，同时获得了学习约束多项式函子代数的新算法.
英文摘要：
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.
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