Abstract
Cellular learning automata is a combination of learning automata and cellular automata. This model is superior to cellular learning automata because of its ability to learn and also is superior to single learning automaton because it is a collection of learning automata which can interact together. In some applications such as image processing, a type of cellular learning automata in which the action of each cell in the next stage of its evolution not only depends on the local environment (actions of its neighbors) but it also depends on the external environments. We call such a cellular learning automata as open cellular learning automata. In this paper, we introduce open cellular learning automata and then study its steady state behavior. It is shown that for a class of rules called commutative rules, the open cellular learning automata in stationary external environments converges to a stable and compatible configuration. Then the application of this new model to image segmentation has been presented.
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