ABSTRACT

Presents a number of new and potentially useful self-learning (adaptive) control algorithms and theoretical as well as practical results for both unconstrained and constrained finite Markov chains-efficiently processing new information by adjusting the control strategies directly or indirectly.

chapter 1|44 pages

Controlled Markov Chains

part I|2 pages

Unconstrained Markov Chains

chapter 2|22 pages

Lagrange Multipliers Approach

chapter 3|18 pages

Penalty Function Approach

chapter 4|28 pages

Projection Gradient Method

part II|2 pages

Constrained Markov Chains

chapter 5|24 pages

Lagrange Multipliers Approach

chapter 6|26 pages

Penalty Function Approach

chapter 7|22 pages

N onregular Markov Chains

chapter 8|76 pages

Practical Aspects