Coalgebra learning via duality

Simone Barlocco; Clemens Kupke; Jurriaan Rot

doi:10.1007/978-3-030-17127-8_4

Coalgebra learning via duality

Simone Barlocco, Clemens Kupke, Jurriaan Rot

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

4 Citations (Scopus)

11 Downloads (Pure)

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.

Original language	English
Title of host publication	Foundations of Software Science and Computation Structures - 22nd International Conference, FOSSACS 2019, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2019, Proceedings
Subtitle of host publication	22nd International Conference, FOSSACS 2019 Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2019 Prague, Czech Republic, April 6–11, 2019 Proceedings
Editors	Mikołaj Bojańczyk, Alex Simpson
Place of Publication	Cham, Switzerland
Publisher	Springer
Pages	62-79
Number of pages	18
ISBN (Print)	9783030171261, 9783030171278
DOIs	https://doi.org/10.1007/978-3-030-17127-8_4
Publication status	Published - 5 Apr 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11425 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Keywords

automata learning
coalgebras
Hennessy-Milner logic
abstract learning algorithm

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Barlocco-etal-FOSSACS2019-Coalgebra-learning-via-dualityFinal published version, 392 KBLicence: CC BY 4.0

https://link.springer.com/book/10.1007/978-3-030-17127-8

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Clemens Kupke
- clemens.kupke@strath.ac.uk
- Computer And Information Sciences - Lecturer B
- SICSA
Person: Academic

1 Finished

Coalgebraic Foundations of Semi-Structured Data (EPSRC First Grant)
Kupke, C.
EPSRC (Engineering and Physical Sciences Research Council)
1/02/16 → 31/01/18
Project: Research

Cite this

Barlocco, S., Kupke, C., & Rot, J. (2019). Coalgebra learning via duality. In M. Bojańczyk, & A. Simpson (Eds.), Foundations of Software Science and Computation Structures - 22nd International Conference, FOSSACS 2019, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2019, Proceedings: 22nd International Conference, FOSSACS 2019 Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2019 Prague, Czech Republic, April 6–11, 2019 Proceedings (pp. 62-79). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11425 LNCS). Springer. https://doi.org/10.1007/978-3-030-17127-8_4

Barlocco, Simone ; Kupke, Clemens ; Rot, Jurriaan. / Coalgebra learning via duality. Foundations of Software Science and Computation Structures - 22nd International Conference, FOSSACS 2019, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2019, Proceedings: 22nd International Conference, FOSSACS 2019 Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2019 Prague, Czech Republic, April 6–11, 2019 Proceedings. editor / Mikołaj Bojańczyk ; Alex Simpson. Cham, Switzerland : Springer, 2019. pp. 62-79 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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Barlocco, S , Kupke, C & Rot, J 2019, Coalgebra learning via duality. in M Bojańczyk & A Simpson (eds), Foundations of Software Science and Computation Structures - 22nd International Conference, FOSSACS 2019, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2019, Proceedings: 22nd International Conference, FOSSACS 2019 Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2019 Prague, Czech Republic, April 6–11, 2019 Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11425 LNCS, Springer, Cham, Switzerland, pp. 62-79. https://doi.org/10.1007/978-3-030-17127-8_4

Coalgebra learning via duality. / Barlocco, Simone ; Kupke, Clemens; Rot, Jurriaan.

Foundations of Software Science and Computation Structures - 22nd International Conference, FOSSACS 2019, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2019, Proceedings: 22nd International Conference, FOSSACS 2019 Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2019 Prague, Czech Republic, April 6–11, 2019 Proceedings. ed. / Mikołaj Bojańczyk; Alex Simpson. Cham, Switzerland : Springer, 2019. p. 62-79 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11425 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

TY - GEN

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N2 - 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.

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Barlocco S , Kupke C, Rot J. Coalgebra learning via duality. In Bojańczyk M, Simpson A, editors, Foundations of Software Science and Computation Structures - 22nd International Conference, FOSSACS 2019, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2019, Proceedings: 22nd International Conference, FOSSACS 2019 Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2019 Prague, Czech Republic, April 6–11, 2019 Proceedings. Cham, Switzerland: Springer. 2019. p. 62-79. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-17127-8_4

Coalgebra learning via duality

Abstract

Publication series

Keywords

Access to Document

Clemens Kupke

Coalgebraic Foundations of Semi-Structured Data (EPSRC First Grant)

Cite this