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A polynomial time incremental algorithm for learning DFA

  • Rajesh Parekh
  • Codrin Nichitiu
  • Vasant Honavar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1433)

Abstract

We present an efficient incremental algorithm for learning deterministic unite state automata (DFA) from labeled examples and membership queries. This algorithm is an extension of Angluin's ID procedure to an incremental framework. The learning algorithm is intermittently provided with labeled examples and has access to a knowledgeable teacher capable of answering membership queries. The learner constructs an initial hypothesis from the given set of labeled examples and the teacher's responses to membership queries. If an additional example observed by the learner is inconsistent with the current hypothesis then the hypothesis is modified minimally to make it consistent with the new example. The update procedure ensures that the modified hypothesis is consistent with all examples observed thus far. The algorithm is guaranteed to converge to a minimum state DFA corresponding to the target when the set of examples observed by the learner includes a live complete set. We prove the convergence of this algorithm and analyze its time and space complexities.

Keywords

Regular Language Learner Construct Incremental Algorithm State Transition Diagram Membership Query 
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 1998

Authors and Affiliations

  • Rajesh Parekh
    • 1
  • Codrin Nichitiu
    • 2
  • Vasant Honavar
    • 3
  1. 1.Allstate Research and Planning CenterMenlo ParkUSA
  2. 2.Ecole Normale Superieure de LyonLyon Cedex 07France
  3. 3.Department of Computer ScienceIowa State UniversityAmesUSA

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