Genetic algorithms for multi-agent fusion-system learning

The development of efficient semi-automatic systems for heterogeneous information fusion is actually a great challenge. The efficiency can be represented as the system openness, the system evolution capabilities and the system performance. Multi-agent architecture can be designed in order to respect the first two efficiency constraints. As for third constraint, which is the performance, the key point is the interaction between each information component of the system. The context of this study is the development of a semi-automatic information fusion system for cartographic features interpretation. Combining heterogeneous sources of information such as expert rules and strategies, domain models, image processing tools, interpolation techniques, etc. completes the system development task. The information modeling and fusion is performed within the evidential theory concepts. The purpose of this article is to propose a learning approach for interaction-oriented multi-agent systems. The optimization of the interaction weight is tackled with genetic algorithms technique because it provides solution for the whole set of weights at once. In this paper, the context of the multi-agent system development is presented first, The need for such system and its parameters is explained. A brief review of learning techniques leads to genetic algorithms as a choice for the learning of the developed multi-agent system. After a brief introduction to genetic algorithms, these are adapted to the particularity of this study. Two approaches are designed to measure the system's fitness based on either binary or fuzzy decisions. The conclusion presents suggestions for further research in the area of multi-agent system-learning with genetic algorithms.