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Algorithms for Learning Finite Automata from Queries: A Unified View

In: Advances in Algorithms, Languages, and Complexity

Author

Listed:
  • José L. Balcázar

    (Universitat Politècnica de Catalunya, Department of Software (LSI))

  • Josep Díaz

    (Universitat Politècnica de Catalunya, Department of Software (LSI))

  • Ricard Gavaldà

    (Universitat Politècnica de Catalunya, Department of Software (LSI))

  • Osamu Watanabe

    (Tokyo Institute of Technology, Department of Computer Science)

Abstract

In this survey we compare several known variants of the algorithm for learning deterministic finite automata via membership and equivalence queries. We believe that our presentation makes it easier to understand what is going on and what the differences between the various algorithms mean. We also include the comparative analysis of the algorithms, review some known lower bounds, prove a new one, and discuss the question of parallelizing this sort of algorithm.

Suggested Citation

  • José L. Balcázar & Josep Díaz & Ricard Gavaldà & Osamu Watanabe, 1997. "Algorithms for Learning Finite Automata from Queries: A Unified View," Springer Books, in: Ding-Zhu Du & Ker-I Ko (ed.), Advances in Algorithms, Languages, and Complexity, pages 53-72, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4613-3394-4_2
    DOI: 10.1007/978-1-4613-3394-4_2
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