Learning real-time automata

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.