Autonomous Sensing and Deciding Framework Processor

Our objective is to develop an innovative cognitive knowledge-aided information processing framework to take very high rate intelligence data streams over wide areas and autonomously highlight AOIs and targets for the image analyst without a priori knowledge of the area or location of the individual high interest targets. We will design, implement, test, and demonstrate an initial multi-agent autonomous sensing and deciding framework, including the development of knowledge bases, detecting anomalies between a real-time scene and the knowledge bases, mining the knowledge bases for new patterns and relationships, monitoring Areas of Interest specified by the analyst, and provide automated machine learning and ranking of anomalies within the multi-agent framework. Based on traffic pattern analysis and pattern of life, we derived knowledge bases and apply sound statistical analysis to detect changes between the current real-time scene and the derived knowledge bases. Through the use of Relevance Vector Machines, machine learning agents learn the important characteristics of AOIs and individual targets.