Learning Pattern Languages over Groups

  • Rupert Hölzl
  • Sanjay Jain
  • Frank Stephan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9925)


This article studies the learnability of classes of pattern languages over automatic groups. It is shown that the class of bounded unions of pattern languages over finitely generated Abelian automatic groups is explanatorily learnable. For patterns in which variables occur at most n times, it is shown that the classes of languages generated by such patterns as well as their bounded unions are, for finitely generated automatic groups, explanatorily learnable by an automatic learner. In contrast, automatic learners cannot learn the unions of up to two arbitrary pattern languages over the integers. Furthermore, there is an algorithm which, given an automaton describing a group G, generates a learning algorithm \(M_G\) such that either \(M_G\) explanatorily learns all pattern languages over G or there is no learner for this set of languages at all, not even a non-recursive one. For some automatic groups, non-learnability results of natural classes of pattern languages are provided.


Abelian Group Abelian Subgroup Finite Index Verbal Language Hypothesis Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank the referees for detailed comments that helped to improve the presentation of this article.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute 1, Faculty of Computer ScienceUniversität der Bundeswehr MünchenNeubibergGermany
  2. 2.School of ComputingNational University of SingaporeSingaporeRepublic of Singapore
  3. 3.Department of MathematicsNational University of SingaporeSingaporeRepublic of Singapore

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