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In general non-determinism, by introducing a degree of parallelism, may increase the accepting power of a model of computation. But if we subject NFAs to the same sort of analysis as we have used in de?ning DFAs we shall see that to simulate an NFA one needs only track ?nitely much information about each string. Consider, again, the example in which we modeled the computation of the NFA as a set of automata processing the input synchronously. In order to determine if a string w is accepted by the NFA all we need to do is to track, at each stage of the computation (i.e., at each pre?x of the input), the states of those automata. Since there is never any reason to include more than one automaton for each state, this will just be some subset of Q-in fact, it is easy to see that the set of states after processing w will be just ˆ δ(q0,w). Since Q is ?nite, it has ?nitely many subsets. Thus we can simulate an NFA with state set Q with a DFA that has a state for each subset of Q. The process of constructing a deterministic analog of a non-deterministic machine is known as determinization.
Sketch an algorithm for the universal recognition problem for SL 2 . This takes an automaton and a string and returns TRUE if the string is accepted by the automaton, FALSE otherwi
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De?nition Deterministic Finite State Automaton: For any state set Q and alphabet Σ, both ?nite, a ?nite state automaton (FSA) over Q and Σ is a ?ve-tuple (Q,Σ, T, q 0 , F), w
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It is not hard to see that ε-transitions do not add to the accepting power of the model. The underlying idea is that whenever an ID (q, σ v) directly computes another (p, v) via
As de?ned the powerset construction builds a DFA with many states that can never be reached from Q′ 0 . Since they cannot be reached from Q′ 0 there is no path from Q′ 0 to a sta
We'll close our consideration of regular languages by looking at whether (certain) problems about regular languages are algorithmically decidable.
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