The Cognitive Adaptive Learning Classification and Response for Communications Threats system, (CALCR) uses a case-based reasoning (CBR) and case-based learning (CBL) approach to address issues encountered in a contested RF communications environment. CALCR was the result of a research project that explored new approaches to understanding communications threats and responding with appropriate countermeasures. Modern communications threats may be modified from existing systems, or may be completely new systems, and CALCR enables a response to these unknown or unanticipated threats. CALCR integrates existing properties of CBR, along with several innovations, making it ideal for this problem: the ability for a case library to be extended through CBL as new conditions are encountered; the robustness of CBR in situations where there is missing data, which CALCR addresses with an advanced intelligent similarity measure; the ability to detect classes unknown to the case library through the use of a confidence measure; and the ability to provide a best-attempt solution, when multiple threat classes are matched, through the use of a new approach called the taxonomy reasoner.

We present an interactive learning method that enables a user to iteratively refine a regression model. The user examines the output of the model, visualized as the vertical axis of a 2D scatterplot, and provides corrections by repositioning individual data instances to the correct output level. Each repositioned data instance acts as a control point for altering the learned model, using the geometry underlying the data. We capture the underlying structure of the data as a manifold, on which we compute a set of basis functions as the foundation for learning. Our results show that manifold-based interactive learning improves performance monotonically with each correction, outperforming alternative approaches.

We present an interactive learning method that enables a user to iteratively refine a regression model. The user examines the output of the model, visualized as the vertical axis of a 2D scatterplot, and provides corrections by repositioning individual data points to the correct output level. Each repositioned data point acts as a control point for altering the learned model, using the geometry underlying the data. We capture the underlying structure of the data as a manifold, on which we compute a set of basis functions as the foundation for learning. Our results show that manifold-based interactive learning achieves dramatic improvement over alternative approaches.

In this paper we describe the application of a novel learning and problem solving architecture to the domain of airspace management, where multiple requests for the use of airspace need to be reconciled and managed automatically. The key feature of our "Generalized Integrated Learning Architecture" (GILA) is a set of integrated learning and reasoning (ILR) systems coordinated by a central meta-reasoning executive (MRE). Each ILR learns independently from the same training example and contributes to problem-solving in concert with other ILRs as directed by the MRE. Formal evaluations show that our system performs as well as or better than humans after learning from the same training data. Further, GILA outperforms any individual ILR run in isolation, thus demonstrating the power of the ensemble architecture for learning and problem solving.