Effective behavioral modeling and prediction even when few exemplars are available

While great progress has been made in the lowest levels of data fusion, practical advances in behavior modeling and prediction remain elusive. The most critical limitation of existing approaches is their inability to support the required knowledge modeling and continuing refinement under realistic constraints (e.g., few historic exemplars, the lack of knowledge engineering support, and the need for rapid system deployment). This paper reports on our ongoing efforts to develop Propheteer, a system which will address these shortcomings through two primary techniques. First, with Propheteer we abandon the typical consensus-driven modeling approaches that involve infrequent group decision making sessions in favor of an approach that solicits asynchronous knowledge contributions (in the form of alternative future scenarios and indicators) without burdening the user with endless certainty or probability estimates. Second, we enable knowledge contributions by personnel beyond the typical core decision making group, thereby casting light on blind spots, mitigating human biases, and helping maintain the currency of the developed behavior models. We conclude with a discussion of the many lessons learned in the development of our prototype Propheteer system.