Background/motivation: Greater heterogeneity of sensed data and and the need to draw from a rapidly increasing volume of data and information sources at all levels in future presents new challenges for fusion (no monolithic systems -  including coalition, legacy and emerging systems)
	Fusion which operates at multiple levels of inference in such an information environment coupled with the ESG sensors in the network will require advanced techniques and agents, and the kind of interaction with semantic interoperability (machine to machine "understanding") that DAML will enable.
	The use of meta-data (e.g., sensor characteristics, performance, reliability, "health” -  and sensed environmental data affecting performance and reliability) {pedigree} in addition to sensor reports (is it there, where, what is it? - i.e., detection, location and classification) and decision context in understood ontologies (HUMINT, open source information etc) will enable fusion of data and information from multiple heterogeneous sensors and sources in a much more meaningful way BY THE SYSTEMS/machines themselves in support of humans. (it is demonstrable that humans alone cant deal with such an information environment further complicated by potentially 1000’s  thousands of sensors added to the ESG).
	EEE at SSC will assess markup potential for fusion, assessing where the best place to use markup will exist, etc. Test if sensor marked up data is useful/doesn't present excessive overhead.  Later will assess whether DAML can achieve the kind of interoperability to enable agents to work across heterogeneous systems.