Regular Inference as Vertex Coloring

  • Christophe Costa Florêncio
  • Sicco Verwer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7568)


This paper is concerned with the problem of supervised learning of deterministic finite state automata, in the technical sense of identification in the limit from complete data, by finding a minimal DFA consistent with the data (regular inference).

We solve this problem by translating it in its entirety to a vertex coloring problem. Essentially, such a problem consists of two types of constraints that restrict the hypothesis space: inequality and equality constraints.

Inequality constraints translate to the vertex coloring problem in a very natural way. Equality constraints however greatly complicate the translation to vertex coloring. In previous coloring-based translations, these were therefore encoded either dynamically by modifying the vertex coloring instance on-the-fly, or by encoding them as satisfiability problems. We provide the first translation that encodes both types of constraints together in a pure vertex coloring instance. This offers many opportunities for applying insights from combinatorial optimization and graph theory to regular inference. We immediately obtain new complexity bounds, as well as a family of new learning algorithms which can be used to obtain both exact hypotheses, as well as fast approximations.


Equality Constraint Chromatic Number Graph Coloring Regular Language Coloring Problem 
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-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Christophe Costa Florêncio
    • 1
  • Sicco Verwer
    • 2
  1. 1.Department of Computer ScienceUniversity of AmsterdamThe Netherlands
  2. 2.Institute for Computing and Information SciencesRadboud University NijmegenThe Netherlands

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