Topic
ω-automaton
About: ω-automaton is a research topic. Over the lifetime, 2299 publications have been published within this topic receiving 68468 citations. The topic is also known as: stream automaton & ω-automata.
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TL;DR: In this article, it was shown that minimizing finite automata is NP-hard for almost all classes of automata that extend the class of deterministic automata, and that the same result holds for all finite-automata classes that subsume that class of @dNFAs which accept strings of length at most three.
41 citations
TL;DR: Stochastic automata models find its application in pattern recognition, multimodal search, and learning control because of the stochastic nature in state transitions and input-output relations, which is suitable for modeling learning systems.
Abstract: Publisher Summary Finite automata can be used as mathematical models for systems with finite number of states that admit at discrete time intervals certain inputs and emit certain outputs. Stochastic automata for which the inputs are constant are called autonomous stochastic automata. An autonomous stochastic automaton can be interpreted as a finite-state Markov chain with the same set state. The formulation of stochastic automata can also be employed to describe the behavior of deterministic automata with random inputs. The state and automata equivalence relations and minimization problems in deterministic finite automata can be extended to the case of stochastic automata. The basic idea used in the synthesis of stochastic automata follows the formulation of deterministic automata with random inputs, that is, autonomous stochastic automata are synthesized as deterministic automata with random inputs. Because of the stochastic nature in state transitions and input-output relations stochastic automata are considered suitable for modeling learning systems. Stochastic automata models find its application in pattern recognition, multimodal search, and learning control.
41 citations
TL;DR: This paper exhibits a strong relation between the sand automata configuration space and the cellular automata Configuration space, and induces a compact topology for sand Automata, and a new context in which sand automatta are homeomorphic to Cellular automata acting on a specific subshift.
41 citations
Proceedings Article•
25 Jul 2015TL;DR: This paper introduces UL* -- a learning algorithm for universal automata (the dual of non-deterministic automata); and AL* -- the dual of alternating automata(s), which generalize both universal and non-trivial automata.
Abstract: Nearly all algorithms for learning an unknown regular language, in particular the popular L* algorithm, yield deterministic finite automata. It was recently shown that the ideas of L* can be extended to yield non-deterministic automata, and that the respective learning algorithm, NL*, outperforms L* on randomly generated regular expressions.
We conjectured that this is due to the existential nature of regular expressions, and NL* might not outperform L* on languages with a universal nature. In this paper we introduce UL* -- a learning algorithm for universal automata (the dual of non-deterministic automata); and AL* -- a learning algorithm for alternating automata (which generalize both universal and non-deterministic automata). Our empirical results illustrate the advantages and trade-offs among L*, NL*, UL* and AL*.
41 citations
TL;DR: A set of so-called well-behaved finite automata that, modulo bisimulation equivalence, corresponds exactly to the set of regular expressions is defined, and it is shown how to determine whether a given finite automaton is in this set.
Abstract: We solve an open question of Milner [1984]. We define a set of so-called well-behaved finite automata that, modulo bisimulation equivalence, corresponds exactly to the set of regular expressions, and we show how to determine whether a given finite automaton is in this set. As an application, we consider the star height problem.
41 citations