@inproceedings{d1f84845349d4b43b9ad74fdfdc46bf8,
title = "A Bayesian network framework for stochastic discrete-event control",
abstract = "This article focuses on the use of Bayesian networks for stochastic Discrete-Event control applications. Bayesian networks offer several advantages for such applications, including a well-developed suite of efficient inference algorithms, model generality and compactness, and ease of model construction and/or model-learning. We show how we can formalise the control-theoretic semantics of a stochastic discrete-event control representation using a Bayesian network. We prove the space-efficiency of a Bayesian network relative to a probabilistic finite state machine. We demonstrate our approach on a simple elevator system.",
author = "Gregory Provan",
year = "2006",
doi = "10.1109/acc.2006.1657689",
language = "English",
isbn = "1424402107",
series = "Proceedings of the American Control Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6039--6044",
booktitle = "Proceedings of the 2006 American Control Conference",
address = "United States",
note = "2006 American Control Conference ; Conference date: 14-06-2006 Through 16-06-2006",
}