  In a vision system, every task needs that the operators to apply should be
{\guillemotleft} well chosen {\guillemotright} and their parameters should be
also {\guillemotleft} well adjusted {\guillemotright}. The diversity of
operators and the multitude of their parameters constitute a big challenge for
users. As it is very difficult to make the {\guillemotleft} right
{\guillemotright} choice, lack of a specific rule, many disadvantages appear
and affect the computation time and especially the quality of results. In this
paper we present a multi-agent architecture to learn the best operators to
apply and their best parameters for a class of images. Our architecture
consists of three types of agents: User Agent, Operator Agent and Parameter
Agent. The User Agent determines the phases of treatment, a library of
operators and the possible values of their parameters. The Operator Agent
constructs all possible combinations of operators and the Parameter Agent, the
core of the architecture, adjusts the parameters of each combination by
treating a large number of images. Through the reinforcement learning
mechanism, our architecture does not consider only the system opportunities but
also the user preferences.
