Decision Aid for Counter-Terrorist Analysts (DACTA)

We propose to develop and demonstrate a Decision Aid for Counter-Terrorist Analysts (DACTA) by fusing and aggregating information from distributed sources. The DACTA architecture integrates a key decision aiding module with two supporting modules forinformation visualization and scenario simulation. The decision-aiding module provides the key decision-support functionalities at multiple levels: 1) low-level fusion of data into task-relevant information, supporting skill-based processing; 2)aggregation of information providing high-level abstraction of task-relevant knowledge, supporting rule-based processing; and 3) response recommendations based on the current context and prior knowledge, supporting knowledge-based processing. Robustperformance is assured through the use of complementary artificial intelligence (AI) techniques (e.g., fuzzy logic, belief networks, probabilistic argumentation), thus providing strong foundations for quantifying uncertainty associated with terroristthreat assessment results from DACTA. The visualization module displays the information produced by the decision-aiding module in a format best suited to the current task, and the analyst's decision-making style and training. DACTA will incorporate asimulation environment supporting speculative analysis and exploration of model-generated hypotheses, via realistic simulated terrorist attack events. We intend to rapidly develop a prototype based on our in-house fusion and belief network engines, anddemonstrate its validity in the context of a bioterrorism scenario.