An algorithm for learning real-time automata (extended abstract)

A common model for discrete event systems is a deterministic finite automaton (DFA). An advantage of this model is that it can be interpreted by domain experts. When observing a real-world system, however, there often is more information than just the sequence of discrete events: the time at which these events occur may be very important. In such a case, the DFA model is too limited. A variant of a DFA that includes the notion of time is called a timed automaton (TA). In this model, each symbol of a word occurs at a certain point in time. The execution of a TA depends not only on the type of symbol occurring, but also on the time that has elapsed since some previous symbol occurrence. We are interested in the problem of identifying such a time dependent system from a data sample. Full paper is published in: Proceedings of the Annual Belgian-Dutch Machine Learning Conference (Benelearn), 2007 See: http://resolver.tudelft.nl/uuid:a202b4cf-5153-4ad5-b41d-5d0332bf04f2