Commander’s Learning Agent (CLearn)

Traditional decision aid software requires the manual input of commander’s intent in the field, which is impractical as it requires an excessive amount of time to learn the various facets of the commander’s job, and requires an unrealistic adaptation capability as the commander’s mission changes dynamically in the field. Hence, there is a need to automatically capture the commander’s current mission, augment with contextual knowledge, and assign priorities to resources supporting the commander’s mission. Technically, this problem falls into the domain of interface agents with learning capability where the agent sits in between the user and application User Interface, passively monitors user behavior in the background, applies machine learning to discover the patterns in the background, and offers to assist the user in the real-time inference mode. Here we propose to develop Commander’s Learning Agent (CLearn) that leverages an existing hybrid Belief network and rule base knowledge management architecture, and prior experience in building such desktop learning agents, and server-based Web personalization agents. While CLearn is relevant to many applications, including information security management, we describe our approach in the context of Intelligence Preparation of the Battlespace support for planning, operations, and assessment missions in Air and Space Operation Centers.