Information required for decision-making in complex applications, such as A©ood forecast or maritime surveillance, can be represented using a mathematical  function. However, due to the complexity of the considered applications and their dynamics, the parameters involved in the mathematical function can be hard to value a priori. This paper presents a Multi-Agent System, called PaMAS (Parameter Multi-Agent System) that is able to learn such parameters values on the A©y, autonomously, cooperatively and by self-adaptation. It also illustrates the application of PaMAS in the context of the maritime surveillance European project I2C. It A©nally provides an evaluation of the PaMAS learning.
['Nicolas Brax', 'Eric Andonoff', 'Marie Pierre Gleizes', 'Pierre Glize']
Self-adaptive Aided Decision-making - Application to Maritime Surveillance.