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Gray-Box Learning of Serial Compositions of Mealy Machines

  • Andreas Abel
  • Jan Reineke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9690)

Abstract

We study the following gray-box learning problem: Given the serial composition of two Mealy machines A and B, where A is known and B is unknown, the goal is to learn a model of B using only output and equivalence queries on the composed machine.

We introduce an algorithm that solves this problem, using at most |B| equivalence queries, independently of the size of A. We discuss its efficient implementation and evaluate the algorithm on existing benchmark sets as well as randomly-generated machines.

Keywords

Output Sequence Main Loop Serial Composition Deterministic Finite Automaton Output Symbol 
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.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceSaarland UniversitySaarbückenGermany

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