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Learning real-time automata
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AI
DOI：10.1007/s11432-019-2767-4.png)
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Abstract
En 中文
Real-time automata (RTAs) are a subclass of timed automata with only one clock which resets at each transition. In this paper, we present an active learning algorithm for deterministic real-time automata (DRTAs) in both continuous-time semantics and discrete-time semantics. For a target language recognized by a DRTA A, we convert the problem of learning DRTA A to the problem of learning a canonical real-time automaton A with the same recognized language, i.e., L(A) = L(A). The algorithm is inspired by existing learning algorithms for symbolic automata.
Keywords:
automaton learning
active learning
real-time automata
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