Coalgebra learning via duality

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationFoundations 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 publication22nd 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
EditorsMikołaj Bojańczyk, Alex Simpson
Place of PublicationCham, Switzerland
PublisherSpringer
Pages62-79
Number of pages18
ISBN (Print)9783030171261, 9783030171278
DOIs
Publication statusPublished - 5 Apr 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11425 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

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

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