Self-adaptive Aided Decision-making - Application to Maritime Surveillance

Abstract

Information required for decision-making in complex applications, such as flood 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 fly, autonomously, cooperatively and by self-adaptation. It also illustrates the application of PaMAS in the context of the maritime surveillance European project I2C. It finally provides an evaluation of the PaMAS learning.