Efficient Learning of Typical Finite Automata from Random Walks

Yoav Freund, Michael Kearns, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

This paper describes new and efficient algorithms for learning deterministic finite automata. Our approach is primarily distinguished by two features: (1) the adoption of an average-case setting to model the "typical" labeling of a finite automaton, while retaining a worst-case model for the underlying graph of the automaton, along with (2) a learning model in which the learner is not provided with the means to experiment with the machine, but rather must learn solely by observing the automaton's output behavior on a random input sequence. The main contribution of this paper is in presenting the first efficient algorithms for learning non-trivial classes of automata in an entirely passive learning model. We adopt an on-line learning model in which the learner is asked to predict the output of the next state, given the next symbol of the random input sequence; the goal of the learner is to make as few prediction mistakes as possible. Assuming the learner has a means of resetting the target machine to a fixed start state, we first present an efficient algorithm that makes an expected polynomial number of mistakes in this model. Next, we show how this first algorithm can be used as a subroutine by a second algorithm that also makes a polynomial number of mistakes even in the absence of a reset. Along the way, we prove a number of combinatorial results for randomly labeled automata. We also show that the labeling of the states and the bits of the input sequence need not be truly random, but merely semi-random. Finally, we discuss an extension of our results to a model in which automata are used to represent distributions over binary strings.

Original languageEnglish (US)
Pages (from-to)23-48
Number of pages26
JournalInformation and Computation
Volume138
Issue number1
DOIs
StatePublished - Oct 10 1997
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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