Abstract
| 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 | |
| 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 |
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Keywords
- automata learning
- coalgebras
- Hennessy-Milner logic
- abstract learning algorithm
Cite this
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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
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 - 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
A2 - Bojańczyk, Mikołaj
A2 - Simpson, Alex
PB - Springer
CY - Cham, Switzerland
ER -