Learning automata approach to hierarchical multiobjective analysis

A novel approach to hierarchical multiobjective analysis using the theory of learning automata is introduced. The problem is modeled as several hierarchies of automata involved in stochastic identical payoff games at the various levels. It is shown that if suitable learning algorithms are chosen at all the levels, the overall performance of the system will improve at each stage. The relevance of the model to multilevel optimization problems is illustrated by considering a simple problem of labeling images consistently