Neural networks to depict the knowledge of multi-agents systems: application to image processing

Our purpose is, in medium term, to detect in air images, characteristic shapes and objects such as airports, industrial plants, planes, tanks, trucks, ... with great accuracy and low rate of mistakes. However, we also want to value whether the link between neural networks and multi-agent systems is relevant and effective. If it appears to be really effective, we hope to use this kind of technology in other fields. That would be an easy and convenient way to depict and to use the agents´ knowledge which is distributed and fragmented. After a first phase of preliminary tests to know if agents are able to give relevant information to a neural network, we verify that only a few agents running on an image are enough to inform the network and let it generalize the agents´ distributed and fragmented knowledge. In a second phase, we developed a distributed architecture allowing several multi-agents systems running at the same time on different computers with different images. All those agents send information to a “multi neural networks system” whose job is to identify the shapes detected by the agents. The name we gave to our project is Jarod