Dynamic programming for deterministic discrete-time systems with uncertain gain

Authors:
Gert de Cooman;Matthias C. M. Troffaes
Affiliations:
Onderzoeksgroep SYSTeMS, Universiteit Gent, Technologiepark, Zwijnaarde 914, 9052 Zwijnaarde, Belgium;Onderzoeksgroep SYSTeMS, Universiteit Gent, Technologiepark, Zwijnaarde 914, 9052 Zwijnaarde, Belgium
Venue:
International Journal of Approximate Reasoning
Year:
2005

Citing 2
Cited 4

Bounded-parameter Markov decision process

Artificial Intelligence
Dynamic Programming

Dynamic Programming

Decision making under uncertainty using imprecise probabilities

International Journal of Approximate Reasoning
Information processing under imprecise risk with an insurance demand illustration

International Journal of Approximate Reasoning
Finite approximations to coherent choice

International Journal of Approximate Reasoning
Implementation of dynamic programming for chaos control in discrete systems

Journal of Computational and Applied Mathematics

Quantified Score

Hi-index	0.00

Visualization

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.