Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9651740 | International Journal of Approximate Reasoning | 2005 | 22 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Gert de Cooman, Matthias C.M. Troffaes,