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A Categorical Framework for Learning Generalised Tree Automata
[article]
2022
arXiv
pre-print
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
arXiv:2001.05786v2
fatcat:asqpica7evhybacn4zmwzwamxm