Team-based Fusion Agents for Warrior's Edge

Sponsor: Army Research Lab

         The goal of this research is to develop team-based agent technologies for enhancing the information fusion (especially level 2 fusion) of DCGS (Distributed Common Ground Station), which serves not only as the gateway between local units and the global “net” but also as a facilitator for information fusion occurring at different levels. There are at least two issues related to the effectiveness of such a vision. First, information fusion and exchanges between the global net, DCGS, and the local units need to adapt to the dynamic battle fields situations, which includes, but not limited to, new information from other sources, and decisions made by local units. Second, effective information fusion within DCGS needs to combine information collected locally with those obtained globally. Third, DCGS should guide, facilitate, and support information fusion of the local units.

          As defined in JDL Data Fusion Process Model, level 2 fusion processing involves understanding the relationships among entities identified by level one processing, the relationship of these entities to the environment, and aggregation of entities for a higher level of understanding and assessment about the situation. More specifically, level 2 fusion includes object aggregation, event/activity aggregation, contextual interpretation, and multi-perspective assessment. Object aggregation analyzes objects that are in geographical proximity to determine if these objects are functionally related. Event/activity aggregation analyzes events and activities in time to identify their associations. Contextual interpretation analyzes the environmental, weather, socio-political context in which level 1 entities are being viewed. Multi-perspective assessment constructs the view of the enemy (i.e., the red view), the “neutral” view of the situation (i.e., the white view), and the view of the friendly forces (i.e., the blue view). These different components of level 2 fusion processing are inter-related. For example, the result of contextual interpretation should be used for object aggregation. Different perspectives of the battlefield are formed by other elements of level 2 processing.

          This research addresses these technical challenges in three novel ways. First, we will augment DCGS with a team of level 2 information fusion agents with “shared mental models” between DCGS and the local units, and between DCGS and the global net. These shared mental models will enable the global net to adapt its information fusion based on dynamic needs of the local units. They will also enable and guide the entity/activity centric information fusion activities of the DCGS and local units. Second, based on the key functionalities of level-2 fusion processing, multiple agents will be developed with complementary capabilities. Together, they form a team that can coordinate and cooperate effectively to deal with the inter-relationships between elements of level-2 fusion. Third, to provide a general framework for agent-based level 2 information fusion, these agents will incorporate a novel situation awareness model based on naturalistic decision making.

          The proposed research will be lead by Dr. John Yen, Dr. Dave Hall, Dr. Mike McNeese, and Dr. Sashi Phoha.