Angluin Learning via Logic

  • Simone Barlocco
  • Clemens KupkeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10703)


In this paper we will provide a fresh take on Dana Angluin’s algorithm for learning using ideas from coalgebraic modal logic. Our work opens up possibilities for applications of tools & techniques from modal logic to automata learning and vice versa. As main technical result we obtain a generalisation of Angluin’s original algorithm from DFAs to coalgebras for an arbitrary finitary set functor T in the following sense: given a (possibly infinite) pointed T-coalgebra that we assume to be regular (i.e. having an equivalent finite representation) we can learn its finite representation by asking (i) “logical queries” (corresponding to membership queries) and (ii) making conjectures to which the teacher has to reply with a counterexample. This covers (a known variant) of the original L* algorithm and the learning of Mealy/Moore machines. Other examples are bisimulation quotients of (probabilistic) transition systems.


Automata learning Coalgebra Modal logic 



The authors would like to thank Nick Bezhanishvili and Alexandra Silva for helpful discussions and pointers to the literature.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Computer and Information SciencesUniversity of StrathclydeGlasgowScotland

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