Dynamic programming for deterministic discrete-time systems with uncertain gain

被引：20
|
作者：
de Cooman, G [1 ]
Troffaes, MCM [1 ]
机构：
[1] Univ Ghent, Onderzoeksgrp SYSTeMS, B-9052 Zwijnaarde, Belgium
来源：
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING | 2005年 / 39卷 / 2-3期
关键词：
optimal control; dynamic programming; uncertainty; imprecise probabilities; lower previsions; sets of probabilities;
D O I：
10.1016/j.ijar.2004.10.004
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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. (c) 2004 Published by Elsevier Inc.
引用
收藏
页码：257 / 278
页数：22
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