Efficiently learning simple timed automata

Abstract. We describe an efficient algorithm for learning deterministic real-time automata (DRTA) from positive data. This data can be obtained from observations of the process to be modeled. The DRTA model we learn from such data can be used reason and gain knowledge about real-time systems such as network protocols, business processes, reactive sys-tems, etc.