Showing papers in "IEEE Transactions on Systems, Man, and Cybernetics in 1981"

A Hierarchical System of Learning Automata

Abstract: A learning automaton operating in a random environment updates its action probabilities on the basis of the reactions of the environment, so that asymptotically it chooses the optimal action. When the number of actions is large the automaton becomes slow because there are too many updatings to be made at each instant. A hierarchical system of such automata with assured c-optimality is suggested to overcome that problem.The learning algorithm for the hierarchical system turns out to be a simple modification of the absolutely expedient algorithm known in the literature. The parameters of the algorithm at each level in the hierarchy depend only on the parameters and the action probabilities of the previous level. It follows that to minimize the number of updatings per cycle each automaton in the hierarchy need have only two or three actions.

On the accuracy of pixel relaxation labeling

Abstract: An analysis of pixel labeling by probabilistic relaxation techniques is presented to demonstrate that these labeling procedures degenerate to weighted averages in the vicinity of fixed points. A consequence of this is that undesired label conversions can occur, leading to a deterioration of labeling accuracy at a stage after an improvement has already been achieved. Means for overcoming the accuracy deterioration are suggested and used as the basis for a possible design strategy for using probabilistic relaxation procedures. The results obtained are illustrated using simple data sets in which labeling on individual pixels can be examined and also using Landsat imagery to show application to data typical of that encountered in remote sensing applications.