Proceedings of SPIE Defense & Security, vol. 6567, Orlando, FL (May)
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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