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- W1968802713 abstract "We describe a minimization procedure for nondeterministic Buchi automata (NBA). For an automaton A another automaton A_min with the minimal number of states is learned with the help of a SAT-solver. This is done by successively computing automata A' that approximate A in the sense that they accept a given finite set of positive examples and reject a given finite set of negative examples. In the course of the procedure these example sets are successively increased. Thus, our method can be seen as an instance of a generic learning algorithm based on a minimally adequate teacher in the sense of Angluin. We use a SAT solver to find an NBA for given sets of positive and negative examples. We use complementation via construction of deterministic parity automata to check candidates computed in this manner for equivalence with A. Failure of equivalence yields new positive or negative examples. Our method proved successful on complete samplings of small automata and of quite some examples of bigger automata. We successfully ran the minimization on over ten thousand automata with mostly up to ten states, including the complements of all possible automata with two states and alphabet size three and discuss results and runtimes; single examples had over 100 states." @default.
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- W1968802713 date "2012-10-07" @default.
- W1968802713 modified "2023-10-18" @default.
- W1968802713 title "Learn with SAT to Minimize Büchi Automata" @default.
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- W1968802713 doi "https://doi.org/10.4204/eptcs.96.6" @default.
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