Heerdt, GV;
Kappé, T;
Rot, J;
Sammartino, M;
Silva, A;
(2022)
A Categorical Framework for Learning Generalised Tree Automata.
In:
Coalgebraic Methods in Computer Science.
(pp. pp. 67-87).
Springer Nature: Cham, Switzerland.
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Abstract
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 \mathtt {L}^{star} algorithm, which was missing from CALF. We instantiate the abstract theory to a large class of \mathbf {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.
| Type: | Proceedings paper |
|---|---|
| Title: | A Categorical Framework for Learning Generalised Tree Automata |
| Event: | 16th IFIP WG 1.3 International Workshop, CMCS 2022, Colocated with ETAPS 2022 |
| ISBN-13: | 978-3-031-10735-1 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-031-10736-8_4 |
| Publisher version: | https://doi.org/10.1007/978-3-031-10736-8_4 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10117574 |
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