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Reduction Techniques for Finite (Tree) AutomataKaati, Lisa January 2008 (has links)
Finite automata appear in almost every branch of computer science, for example in model checking, in natural language processing and in database theory. In many applications where finite automata occur, it is highly desirable to deal with automata that are as small as possible, in order to save memory as well as excecution time. Deterministic finite automata (DFAs) can be minimized efficiently, i.e., a DFA can be converted to an equivalent DFA that has a minimal number of states. This is not the case for nondeterministic finite automata (NFAs). To minimize an NFA we need to compute the corresponding DFA using subset construction and minimize the resulting automaton. However, subset construction may lead to an exponential blowup in the size of the automaton and therefore even if the minimal DFA may be small, it might not be feasible to compute it in practice since we need to perform the expensive subset construction. To aviod subset construction we can reduce the size of an NFA using heuristic methods. This can be done by identifying and collapsing states that are equal with respect to some suitable equivalence relation that preserves the language of the automaton. The choice of an equivalence relation is a tradeoff between the desired amount of reduction and the computation time since the coarser a relation is, the more expensive it is to compute. This way we obtain a reduction method for NFAs that is useful in practice. In this thesis we address the problem of reducing the size of nondeterministic automata. We consider two different computation models: finite tree automata and finite automata. Finite automata can be seen as a special case of finite tree automata and all of the previously mentioned results concerning finite automata are applicable to tree automata as well. For nondeterministic bottomup tree automata, we present a broad spectrum of different relations that can be used to reduce their size. The relations differ in their computational complexity and reduction capabilities. We also provide efficient algorithms to compute the relations where we translate the problem of computing a given relation on a tree automaton to the problem of computing the relation on a finite automaton. For finite automata, we have extended and reformulated two algorithms for computing bisimulation and simulation on transition systems to operate on finite automata with alphabets. In particular, we consider a model of automata where the labels are encoded symbolically and we provide an algorithm for computing bisimulation on this partial symbolic encoding.

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