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

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

Verwer, S.E.
De Weerdt, M.M.
Witteveen, C.

Electrical Engineering, Mathematics and Computer Science

Software Computer Technology

2007-11-05

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

http://resolver.tudelft.nl/uuid:daac7b7b-a28f-477d-b2cc-0db00507a4e2

BNAIC 2007: 19th Belgium-Dutch Conference on Artificial Intelligence, Utrecht, The Netherlands, 5-6 November 2007

Institutional Repository

conference paper

(c) 2007 The Author(s)