Modeling complex real world problems in the national and homeland security domains require multi-disciplinary thinking and utilize multiple analytical approaches to represent massive numbers of entities, their behaviors, and the emergent interactions among them. As such, the traditional approach to building comprehensive, requirements-driven simulations does not work for such problems. This project uses a Society-based Approach to Integration using a ?shared but self-managed? paradigm, wherein, autonomous members collaborate in a society while sharing only a part of their knowledge. Component simulations self-assemble into realistic synthetic environments. The self-assembly of simulations is achieved through a domain-specific ontology, simulation specifications, and semantic matching between diverse members. New members join an existing society or an existing member modify its interaction needs without requiring the society to reconfigure. Using knowledge discovery, each member determines what aspects of entities in the society to interact with. In this way, a society is automatically configured into a synthetic environment. Broader impacts of this project include: creation and deployment of large scale synthetic environments by bridging new and existing models and simulations from diverse disciplines; leverage knowledge generated by the wider DDDAS community in creating complex synthetic environments at scales and diversity much greater than the state-of-the-art; facilitate rapid integration across diverse systems and paradigms, such as, discrete event simulations with agent based simulations, in a semantically consistent manner; and develop open source technology that will benefit the community at large with broader application to simulation based engineering, education, and decision analytics.
