Barlocco, S;
Kupke, C;
Rot, J;
(2019)
Coalgebra Learning via Duality.
In: Bojanczyk, M and Simpson, A, (eds.)
Coalgebra Learning via Duality.
(pp. pp. 62-79).
Springer: Cham, Switzerland.
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Abstract
Automata learning is a popular technique for inferring minimal automata through membership and equivalence queries. In this paper, we generalise learning to the theory of coalgebras. The approach relies on the use of logical formulas as tests, based on a dual adjunction between states and logical theories. This allows us to learn, e.g., labelled transition systems, using Hennessy-Milner logic. Our main contribution is an abstract learning algorithm, together with a proof of correctness and termination.
| Type: | Proceedings paper |
|---|---|
| Title: | Coalgebra Learning via Duality |
| Event: | Foundations of Software Science and Computation Structures 22nd International Conference (FoSSaCS 2019), 6-11 April 2019, Prague, Czech Republic |
| ISBN: | 978-3-030-17126-1 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-030-17127-8_4 |
| Publisher version: | https://doi.org/10.1007/978-3-030-17127-8_4 |
| Language: | English |
| Additional information: | © The Author(s) 2019. Open Access. This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creative commons.org/ licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. |
| 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/10075212 |
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