Coalgebra learning via duality

Barlocco, Simone and Kupke, Clemens and Rot, Jurriaan; Bojańczyk, Mikołaj and Simpson, Alex, eds. (2019) Coalgebra learning via duality. In: 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) . Springer, Cham, Switzerland, pp. 62-79. ISBN 9783030171278

Preview

Text (Barlocco-etal-FOSSACS2019-Coalgebra-learning-via-duality)
Barlocco_etal_FOSSACS2019_Coalgebra_learning_via_duality.pdf
Final Published Version
License:

Download (401kB)| Preview

Official URL: https://doi.org/10.1007/978-3-030-17127-8_4

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.

Citations and altmetrics

Item metadata

Item type:	Book Section
ID code:	67613
Dates:	Date Event 5 April 2019 Published 25 January 2019 Accepted
Keywords:	automata learning, coalgebras, Hennessy-Milner logic, abstract learning algorithm, Electronic computers. Computer science, Computer Science(all)
Subjects:	Science > Mathematics > Electronic computers. Computer science
Department:	Faculty of Science > Computer and Information Sciences
Depositing user:	Pure Administrator
Date deposited:	18 Apr 2019 13:15
Last modified:	05 Mar 2021 03:56
Related URLs:	Publisher
URI:	https://strathprints.strath.ac.uk/id/eprint/67613

Export

CORE (COnnecting REpositories)