Proceedings of American Institute of Aeronautics and Astronautics (AIAA) – Infotech@Aerospace, Atlanta, GA (2010)
A number of research efforts have investigated approaches and technologies to improve space situation awareness. Prior research included investigation of probabilistic methods using structured models such as Bayesian belief networks. Such approaches have a firm foundation in evidential reasoning and are believed to have considerable potential for reasoning about situations of interest in space. However, building the necessary models and specifying the quantitative probabilities linking nodes is a non-trivial effort.
Probabilistic models necessitate the estimation of probabilities that relate events. However, situations of interest are rare events and a lack of relevant observations makes it challenging to validate probabilistic models. Research in the elicitation of probabilities has shown that even domain experts cannot effectively generate valid mathematical relationships between abstract entities that form probabilistic models. The size and complexity of probabilistic models that realistically describe situations of interest exacerbates the challenge of defining the required probabilities. Data driven machine learning approaches have been used to address this problem in other domains. However, the lack of exemplars of events of interest limits the applicability of data driven machine learning to the modeling space situations of interest. The result is an increased reliance on elicited knowledge.
Technologies are needed that simplify the process of model construction and that can take advantage of multiple information sources to provide space situation awareness. This paper describes an approach, based on Toulmin’s theory of argumentation, which provides an intuitive representation that can be used to create and exercise models of space situations. These models are then supplied to a fusion component that uses the Dempster-Shafer theory of belief propagation to estimate confidences for the characterization of these situations of interest.
This work was performed under Government contract number FA9453-08-C-0005 with the Air Force Advanced Research Laboratory. The authors thank our sponsors at AFRL/RV for their support and direction on this project.
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