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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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Book ChapterDOI
16 Dec 2004
TL;DR: Real-Counter Automata as mentioned in this paper are two-way finite automata augmented with counters that take real values, which accept real words that are bounded and closed real intervals delimited by a finite number of markers.
Abstract: We introduce real-counter automata, which are two-way finite automata augmented with counters that take real values. In contrast to traditional word automata that accept sequences of symbols, real-counter automata accept real words that are bounded and closed real intervals delimited by a finite number of markers. We study the membership and emptiness problems for one-way/two-way real-counter automata as well as those automata further augmented with other unbounded storage devices such as integer-counters and pushdown stacks.

8 citations

Proceedings ArticleDOI
01 Apr 1990
TL;DR: A result of B. Neumann's Equality Theorem is extended to a general result which implies that the nmltiplicity equivalence problem of two (nondeterministic) multitape finite automata is decidable, and a long standing open problem in automata theory is solved.
Abstract: Using a result of B.II. Neumann we extend Eilenberg's Equality Theorem, [E], to a general result which implies that the nmltiplicity equivalence problem of two (nondeterministic) multitape finite automata is decidable. As a corollary we solve a long standing open problem in automata theory, namely, the equivalence problem for multitape deterministic finite automata. One of the oldest and most falnous proble,ns in au-tomata theory is the equivalence problem for deter-ministic multitape finite automata. The notion of multitape finite automaton, or mvltitape automatoll for short, was introduced by Rabin and Scott in their classic paper of 1959, [RS]. They also showed that, unlike for ordinary (one-tape) finite autonlata, non-deterministic multitape automata are more powerfitl than the deterministic ones. This tiolds already iri the case of two tapes. As a central model of autolnata, multitape all-tomata have gained plenty of attention, llowever, many important problems have reinained ol)en, including the equivalence problem in tile deterministic case. For nondeterministic multitape automata (even for two-tape automata, whicli are nornlally called finite transducers) the equivalence problem is a standard example of an undecidable problem, see [Be]. This undecidability result was first prow~d in 1968 by Griffiths, [G]. Tile equivalence problem of multitape determin-istic automata has, as far as we know, been expected to be decidable. It seems that in tiffs context "equiv-alence" implies "structural similarity". Despite this the equivalence problem has been solved only in a few special cases. The oldest result is that of Bird from 1973,[Bi] which solves the problem for two-tape de-terministic automata. An alternative solution to the two-tape case was given in [W]. Numerous attempts, see [L], [Ki], [CK], to solve the general problem have lead to only modest success so far. The difficulty of thc equivalence problem is already manifested in the fact that the inclusion problem for multitape deter-,ninist.ic automata is easily seen to be undecidable. Our approach is as follows, htstead of determin-istic multitape automata we consider nondeterminis-tic multitape automata with nmltiplicities. Thus we ask whether two given multitape automata are mul-tiplicitly equivalent, that is, whether they accept the same n-tuples of words exactly the same number of tithes. Tile nmltiplicity equivalence clearly reduces to ordinary equivalence if the automata are deter-minisl.ic, and even when they are unambiguous. The multiplicity equiwtlence problem for finite transducers has been considered an important open problem of its own, see [Ka]. Permission to copy without fee all or part of this matertial …

8 citations

Proceedings ArticleDOI
14 Nov 1989
TL;DR: The authors investigate the improvements gained by rendering the pursuit algorithm discrete by restricting the probability of selecting an action to a finite and, hence, discrete subset of the environment.
Abstract: The authors consider the problem of a stochastic learning automaton interacting with an unknown random environment. The fundamental problem is that of learning, through interaction, the best action (that is, the action which is rewarded optimally) allowed by the environment. By using running estimates of reward probabilities to learn the optimal action, an extremely efficient pursuit algorithm was obtained by M.A.L. Thathachar et al. (1986, 1989) which is presently among the fastest-growing algorithms known. In the present work, the authors investigate the improvements gained by rendering the pursuit algorithm discrete. This is done by restricting the probability of selecting an action to a finite and, hence, discrete subset of

8 citations

01 Jan 2003
TL;DR: It is shown that ω -P automata with only two membranes can simulate the computational power of usual (non-deterministic) ω-Turing machines.
Abstract: We introduce ω -P automata based on the model of P systems with membrane channels (see [8]) using only communication rules. We show that ω -P automata with only two membranes can simulate the computational power of usual (non-deterministic) ω -Turing machines. A very restricted variant of ω -P automata allows for the simulation of ω -finite automata in only one membrane.

8 citations

Proceedings ArticleDOI
26 Jul 1993
TL;DR: An online path-planning algorithm for multiple automata that decomposes all simple and complex swarms uniformly by the following dynamic/static conversion: some automata are stopped in the swarm, and the others are moved around the set of stopped automata.
Abstract: An online path-planning algorithm for multiple automata is proposed. Each automaton usually goes straight to the goal. When some of the automata touch each other, some kind of swarm occurs and is identified by sensors. In general, several kinds of swarm appear, since a lot of automata collide in different circumstances. The proposed algorithm decomposes all simple and complex swarms uniformly by the following dynamic/static conversion: some automata are stopped in the swarm, and the others are moved around the set of stopped automata. By using this conversion, the algorithm destroys all swarms by some simple motions in common and is easily applied for some practical robots. Moreover, the automata decompose the swarm by cooperative motions as follows: an inside automaton comes close to its goal monotonously. Since all automata go straight to their goals outside the swarm, convergence of the automata toward their goals is naturally ensured. Communication overhead is kept small enough by using the monotony property, and consequently the algorithm can run online.

8 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20238
202219
20201
20191
20185
201748