Supporting human decision making in tasks such as disaster planning and time-sensitive targeting is challenging because
of the breadth and depth of knowledge that goes into the decision making process and the need to reason with this
knowledge within tight time constraints. Ontologies are well suited for representing the concepts that humans use in
describing the domain of interest. However, ontologies can be costly to develop and, by themselves, are inadequate to
capture the kinds of decision making knowledge that arise in practice-for instance, those that refer to multiple
ontologies or to established precedent. Such decision making knowledge can be represented by using a knowledge
representation formalism that we call decision rules. These decision rules are similar to the rules used in rule based
systems but can (a) include primitives from multiple ontologies and primitives that are defined by algorithms that run
outside of the rule framework (b) be time dependent and (c) incorporate default assumptions. We report on our ongoing
experience in using such a combination of ontologies and decision rules in building a decision support application for
time sensitive targeting.
KEYWORDS: Data modeling, Systems modeling, Data fusion, Information fusion, Logic, Prototyping, Machine learning, Computer programming, Process modeling, Computing systems
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.
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