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Pushdown automaton

About: Pushdown automaton is a research topic. Over the lifetime, 1868 publications have been published within this topic receiving 35399 citations.


Papers
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Journal ArticleDOI
TL;DR: Several well-known classes of formal languages, which had up to date only been characterized by grammars, are characterized by this new automaton of complexity.
Abstract: A new automaton, called a contraction automaton of complexity ( n, k ), n > 0, k > 1, is defined, and several well-known classes of formal languages, which had up to date only been characterized by grammars, are characterized by this new automaton.

16 citations

Book ChapterDOI
01 Sep 1990
TL;DR: Circuit classes exactly characterizing polynomially time bounded unambiguous augmented push-down automata are exhibited.
Abstract: The notions of weak and strong unambiguity of augmented push-down automata are considered and related to unambiguities of circuits. In particular we exhibit circuit classes exactly characterizing polynomially time bounded unambiguous augmented push-down automata.

16 citations

Book ChapterDOI
25 Aug 2008
TL;DR: This article considers parity games defined by higher-order pushdown systems and provides a k- Exptime algorithm to compute finite representations of positional winning strategies for both players for games defined for level-khigher- order pushdown automata.
Abstract: Higher-order pushdown systems generalize pushdown systems by using higher-order stacks, which are nested stacks of stacks. In this article, we consider parity games defined by higher-order pushdown systems and provide a k- Exptime algorithm to compute finite representations of positional winning strategies for both players for games defined by level-khigher-order pushdown automata. Our result is based on automata theoretic techniques exploiting the tree structure corresponding to higher-order stacks and their associated operations.

16 citations

11 May 2006
TL;DR: This work proposes a simple type of timed automaton to model DES where the timing of the events is important, and shows how the currently best learning algorithm for DFAs (state merging) can be adapted to deal with time information.
Abstract: A model for discrete event systems (DES) can be learned from observations. We propose a simple type of timed automaton to model DES where the timing of the events is important. Learning such an automaton is proven to be NP-complete by a reduction from the problem of learning deterministic finite state automata (DFA) without time. Based on this reduction, we show how the currently best learning algorithm for DFAs (state merging) can be adapted to deal with time information.

16 citations

Book ChapterDOI
11 Aug 2011
TL;DR: A great number of experiments and simulations verified the proposed DGSE learning algorithm is quite efficient when operating in P-model stationary environment and pointed out that this proof process could be applied to prove SERI algorithm.
Abstract: New class of P-model absorbing e-optimal learning automata was presented in this paper. The proposed learning automaton, Discretized Generalized Stochastic Estimator (DGSE) learning automaton, not only possesses the characteristics of the Stochastic Estimator Reward-inaction (SERI ) learning automaton and the Discretized Generalized Pursuit Algorithm (DGPA) learning automaton, but also converges with a remarkable speed and accuracy. The asymptotic behavior of the DGSE algorithm is analyzed. Furthermore, we stick out the pitfalls in the proof of SERI algorithm, proved the proposed DGSE algorithm to be e-optimal, and pointed out that this proof process could be applied to prove SERI algorithm. It's known that the SERI learning automaton is the fastest learning automaton up to now, whereas, the proposed DGSE learning automaton is much faster than the SERI learning automaton. A great number of experiments and simulations verified the propose DGSE learning algorithm is quite efficient when operating in P-model stationary environment.

16 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202314
202234
202129
202052
201947
201834