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In asymmetric conflicts, improvised explosive devices (IEDs) are a favoured tool of insurgents. They have long been used by extremists, insurgents and resistant groups and are currently a primary threat in Iraq and Afghanistan. The US military has put a great deal of effort into reducing these types of attacks, ranging from improved methods for locating the devices to social models that attempt to predict where insurgent behavior will spread. By considering behavioral aspects leading up to an IED event as a system, we are able to evaluate intervention strategies that focus on the process by which individuals become involved in insurgent IED activities. This paper presents models of the influences and the behaviors associated with individuals involved in IED-related activities and treats them as a system. This approach allows us to explore societal systems to see how motivation (and deterrents to motivation) may play out. Copyright

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We present a novel ensemble architecture for learning problem-solving techniques from a very small number of expert solutions and demonstrate its effectiveness in a complex real-world domain. The key feature of our "Generalized Integrated Learning Architecture" (GILA) is a set of integrated learning and reasoning (ILR) components, coordinated by a central meta-reasoning executive (MRE). The ILRs are weakly coupled in the sense that all coordination happens through the MRE. Each ILR learns independently from a small number of expert demonstrations of a complex task. During the performance, each ILR proposes partial solutions to subproblems posed by the MRE, which are then selected from and pieced together by the MRE to produce a complete solution. We describe the application of this novel learning and problem solving architecture to the domain of airspace management, where multiple requests for the use of airspaces need to be deconflicted, reconciled and managed automatically. Formal evaluations show that our system performs as well as or better than humans after learning from the same training data. Furthermore, GILA outperforms any individual ILR run in isolation, thus demonstrating the power of the ensemble architecture for learning and problem solving.