We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.

Durham Research Online
You are in:

Dynamic programming for deterministic discrete-time systems with uncertain gain.

De Cooman, G. and Troffaes, M. C. M. (2005) 'Dynamic programming for deterministic discrete-time systems with uncertain gain.', International journal of approximate reasoning., 39 (2-3). pp. 257-278.


We generalise the optimisation technique of dynamic programming for discrete-time systems with an uncertain gain function. We assume that uncertainty about the gain function is described by an imprecise probability model, which generalises the well-known Bayesian, or precise, models. We compare various optimality criteria that can be associated with such a model, and which coincide in the precise case: maximality, robust optimality and maximinity. We show that (only) for the first two an optimal feedback can be constructed by solving a Bellman-like equation.

Item Type:Article
Keywords:Optimal control, Dynamic programming, Uncertainty, Imprecise probabilities, Lower previsions, Sets of probabilities.
Full text:(AM) Accepted Manuscript
Download PDF
Publisher Web site:
Record Created:29 Feb 2008
Last Modified:24 Aug 2011 13:25

Social bookmarking: del.icio.usConnoteaBibSonomyCiteULikeFacebookTwitterExport: EndNote, Zotero | BibTex
Look up in GoogleScholar | Find in a UK Library