The proposed innovation, entitled CiTAPS (Collaborative Intelligent Threat Assessment & Prediction System) combines collaborative software agents, intelligent machine and peer-to-peer technologies to support extensible, interoperable, and fully automated real time indications and warning extraction and dissemination. It provides users with on-demand multi-source data retrieval and fusion, overt threat notification for terrorist activities, and covert threat detection and notification through remote data source monitoring and analysis. Using multiple intelligent software agents deployed to a variety of data sources within a specific domain, CiTAPS is capable of retrieving, fusing, analyzing, and extracting anomalies from multiple sources of heterogeneous data for integration into a Common Relevant Operational Picture (CROP). These agents are able to collaborate within their own domain using standards-based communications for agent frameworks. Our teams approach is to build upon previous research findings on peer-to-peer technologies, leverage those against the Homeland Defenses Intelligent Agent problem, and create a formal standards-based architectural design and an interactive visualization tool outlining the major concepts of the proposed system.
Benefits: Phase I research should yield a well documented design based on evaluated and selected key technologies, and an implementation of a visualization tool that will fit effortlessly into the Phase II prototype system. The Phase II prototype will demonstrate logistical and performance characteristics of the innovation. Phase III and commercialization will provide a robust agent-based information analysis system adaptable to a variety of domains including National Security/Intelligence, healthcare, and the Future Combat System.
Keywords: Intelligent agent, peer to peer, p2p, collaborative decision, indications & warnings, intelligent machine, threat assessment and prediction, Jini
