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According to the problems of visual perception, we propose a model for the processing of vision in adverse situations of illumination, scale, etc. In this paper, a model for image segmentation and labelling obtained in real conditions with different scales is proposed.

The model is based on the texture identification of the scene's objects by means of comparison with a database that stores series of each texture perceived with successive optic parameter values. As a basis for the model, self‐organising maps have been used in several phases of the labelling process.

The model has been conceived to systematically deal with the different causes that make vision difficult and allows it to be applied in a wide range of real situations. The results show high success rates in the labelling of scenes captured in different scale conditions, using very simple describers, such as different histograms of textures.

Our interest is directed towards systematising the proposal and experimenting on the influence of the other variables of the vision. We will also tackle the implantation of the classifier module so that the different causes can be dealt with by the reconfiguration of the same hardware (using reconfigurable hardware).

This research approaches a very advanced angle of the vision problems: visual perception under adverse conditions. In order to deal with this problem, a model formulated with a general purpose is proposed. Our objective is to present an approach to conceive universal architectures (in the sense of being valid with independence of the implied magnitudes).

To provide a formal framework based on the action and reaction model that allows us to cover the dynamics of multi‐agent systems (MAS) made up of mobile software agents suitable for scalable networks.

This model is based on the operation of the human nervous centers. In the case of systems based on mobile agents, the main problem is the different vision the agents have of the world and the impossibility of being aware of and synchronizing all the influences brought by the different agents acting on it.

This proposal has been compared with the conventional MAS by solving an extension of the predator‐prey problem. The results show the advantages of mobility, as the size of the problem grows in a distributed system.

At the present time, the model is being applied in works related to the control of biological systems and also in those related to the network management.

From this formulation, a set of refinements on the conventional approach is proposed that reduces the effect of possible space and time inconsistencies between the agents and the environment and at the same time, achieves the generalization of the reaction function.