Symbolic automata allow transitions to carry predicates over rich alphabet theories, such as linear arithmetic, and therefore extend classic automata to operate over infinite alphabets, such as the set of rational numbers. Existing automata algorithms rely on the alphabet being finite, and generalizing them to the symbolic setting is not a trivial task. In our earlier work, we proposed new techniques for minimizing deterministic symbolic automata and, in this paper, we generalize these techniques and study the foundational problem of computing forward bisimulations of nondeterministic symbolic finite automata. We propose three algorithms. Our first algorithm generalizes Moore\u2019s algorithm for minimizing deterministic automata. Our second algorithm generalizes Hopcroft\u2019s algorithm for minimizing deterministic automata. Since the first two algorithms have quadratic complexity in the number of states and transitions in the automaton, we propose a third algorithm that only requires a number of iterations that is linearithmic in the number of states and transitions at the cost of an exponential worst-case complexity in the number of distinct predicates appearing in the automaton. We implement our algorithms and evaluate them on 3,625 nondeterministic symbolic automata from real-world applications.<\/p>\n
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Symbolic automata allow transitions to carry predicates over rich alphabet theories, such as linear arithmetic, and therefore extend classic automata to operate over infinite alphabets, such as the set of rational numbers. Existing automata algorithms rely on the alphabet being finite, and generalizing them to the symbolic setting is not a trivial task. 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