Hybrid methodology for situation assessment model development within an air operations center domain
2007
Within the dynamic environment of an Air Operations Center (AOC), effective decision-making is highly dependent on
timely and accurate situation assessment. In previous research efforts the capabilities and potential of a Bayesian belief
network (BN) model-based approach to support situation assessment have been demonstrated. In our own prior research,
we have presented and formalized a hybrid process for situation assessment model development that seeks to ameliorate
specific concerns and drawbacks associated with using a BN-based model construct. Specifically, our hybrid
methodology addresses the significant knowledge acquisition requirements and the associated subjective nature of using
subject matter experts (SMEs) for model development. Our methodology consists of two distinct functional elements: an
off-line mechanism for rapid construction of a Bayesian belief network (BN) library of situation assessment models
tailored to different situations and derived from knowledge elicitation with SMEs; and an on-line machine-learning-based
mechanism to learn, tune, or adapt BN model parameters and structure. The adaptation supports the ability to
adjust the models over time to respond to novel situations not initially available or anticipated during initial model
construction, thus ensuring that the models continue to meet the dynamic requirements of performing the situation
assessment function within dynamic application environments such as an AOC. In this paper, we apply and demonstrate
the hybrid approach within the specific context of an AOC-based air campaign monitoring scenario. We detail both the
initial knowledge elicitation and subsequent machine learning phases of the model development process, as well as
demonstrate model performance within an operational context.
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