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

6 Citations (Scopus)

8 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	International Conference on Foundations of Software Science and Computation Structures [FoSSaCS 2019]
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
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Keywords

automata learning
coalgebras
Hennessy-Milner logic
abstract learning algorithm

Access to Document

10.1007/978-3-030-17127-8_4Licence: CC BY 4.0

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

Cite this

Barlocco, S., Kupke, C., & Rot, J. (2019). Coalgebra learning via duality. In M. Bojańczyk, & A. Simpson (Eds.), International Conference on Foundations of Software Science and Computation Structures [FoSSaCS 2019] (pp. 62-79). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11425). Springer. https://doi.org/10.1007/978-3-030-17127-8_4

Barlocco, Simone ; Kupke, Clemens ; Rot, Jurriaan. / Coalgebra learning via duality. International Conference on Foundations of Software Science and Computation Structures [FoSSaCS 2019]. 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.",

keywords = "automata learning, coalgebras, Hennessy-Milner logic, abstract learning algorithm",

author = "Simone Barlocco and Clemens Kupke and Jurriaan Rot",

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Barlocco, S , Kupke, C & Rot, J 2019, Coalgebra learning via duality. in M Bojańczyk & A Simpson (eds), International Conference on Foundations of Software Science and Computation Structures [FoSSaCS 2019]. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11425, 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.

International Conference on Foundations of Software Science and Computation Structures [FoSSaCS 2019]. 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).

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

TY - GEN

T1 - Coalgebra learning via duality

AU - Barlocco, Simone

AU - Kupke, Clemens

AU - Rot, Jurriaan

PY - 2019/4/5

Y1 - 2019/4/5

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.

AB - 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.

KW - automata learning

KW - coalgebras

KW - Hennessy-Milner logic

KW - abstract learning algorithm

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

U2 - 10.1007/978-3-030-17127-8_4

DO - 10.1007/978-3-030-17127-8_4

M3 - Conference contribution book

SN - 9783030171261

SN - 9783030171278

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 62

EP - 79

BT - International Conference on Foundations of Software Science and Computation Structures [FoSSaCS 2019]

A2 - Bojańczyk, Mikołaj

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PB - Springer

CY - Cham, Switzerland

ER -

Barlocco S , Kupke C, Rot J. Coalgebra learning via duality. In Bojańczyk M, Simpson A, editors, International Conference on Foundations of Software Science and Computation Structures [FoSSaCS 2019]. 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

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Clemens Kupke

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

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Coalgebra learning via duality

Abstract

Publication series

Keywords

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Fingerprint

Profiles

Clemens Kupke

Projects

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

Cite this