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One Drop of Non-Determinism in a Random Deterministic Automaton

Authors Arnaud Carayol, Philippe Duchon, Florent Koechlin, Cyril Nicaud

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Author Details

Arnaud Carayol
  • Univ Gustave Eiffel, CNRS, LIGM, F-77454 Marne-la-Vallée, France
Philippe Duchon
  • Univ. Bordeaux, CNRS UMR 5800, LaBRI, F-33400 Talence, France
Florent Koechlin
  • Université de Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France
Cyril Nicaud
  • Univ Gustave Eiffel, CNRS, LIGM, F-77454 Marne-la-Vallée, France

Acknowledgements

The authors would like to thank the reviewers for their helpful comments.

Cite AsGet BibTex

Arnaud Carayol, Philippe Duchon, Florent Koechlin, and Cyril Nicaud. One Drop of Non-Determinism in a Random Deterministic Automaton. In 40th International Symposium on Theoretical Aspects of Computer Science (STACS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 254, pp. 19:1-19:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.STACS.2023.19

Abstract

Every language recognized by a non-deterministic finite automaton can be recognized by a deterministic automaton, at the cost of a potential increase of the number of states, which in the worst case can go from n states to 2ⁿ states. In this article, we investigate this classical result in a probabilistic setting where we take a deterministic automaton with n states uniformly at random and add just one random transition. These automata are almost deterministic in the sense that only one state has a non-deterministic choice when reading an input letter. In our model each state has a fixed probability to be final. We prove that for any d ≥ 1, with non-negligible probability the minimal (deterministic) automaton of the language recognized by such an automaton has more than n^d states; as a byproduct, the expected size of its minimal automaton grows faster than any polynomial. Our result also holds when each state is final with some probability that depends on n, as long as it is not too close to 0 and 1, at distance at least Ω(1/√n) to be precise, therefore allowing models with a sublinear number of final states in expectation.

Subject Classification

ACM Subject Classification
  • Theory of computation → Regular languages
  • Mathematics of computing → Combinatorics
  • Mathematics of computing → Probability and statistics
Keywords
  • non-deterministic automaton
  • powerset construction
  • probabilistic analysis

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