Coalgebra learning via duality

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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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 languageEnglish
Title of host publicationInternational Conference on Foundations of Software Science and Computation Structures [FoSSaCS 2019]
EditorsMikołaj Bojańczyk, Alex Simpson
Place of PublicationCham, Switzerland
Number of pages18
ISBN (Print)9783030171261, 9783030171278
Publication statusPublished - 5 Apr 2019

Publication series

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


  • automata learning
  • coalgebras
  • Hennessy-Milner logic
  • abstract learning algorithm


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