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.
In dynamic environments (e.g. an Air Operations Center (AOC)), effective real-time monitoring of mission execution is highly dependent on situation awareness (SA). But whereas an individual's perception of mission progress is biased by his or her immediate tasks and environment, the combined perspectives of key individuals provides an effects-based assessment of the mission overall. Belief networks (BNs) are an ideal tool for modeling and meeting the requirements of SA: at the individual level BNs emulate a skilled human's information fusion and reasoning process in a multi-task environment in the presence of uncertainty. At the mission level, BNs are intelligently combined to yield a common operating picture. While belief networks offer significant advantages for SA in this manner, the work of defining and combining the models is difficult due to factors such as multiple-counting and conflicting reports. To address these issues, we develop a system consisting of three distinct functional elements: an off-line mechanism for rapid construction of a BN library of SA models tailored to different air combat operation situations and derived from knowledge elicitation with subject matter experts; an off-line mechanism to adapt and combine BN models that supports the ability to adjust the SA models over time and in response to novel situations not initially available or anticipated during model construction; and an on-line combination of SA models to support an enhanced SA and the ability to
monitor execution status in real time and informed by and responsive to the individuals and situations involved.
Situation awareness involves the identification and monitoring of relationships among level-one objects. This problem in general is intractable (i.e., there is a potentially infinite number of relations that could be tracked) and thus requires additional constraints and guidance defined by the user if there is to be any hope of creating practical situation awareness systems. This paper describes a Situation Awareness Assistant (SAWA) that facilitates the development of user-defined domain knowledge in the form of formal ontologies and rule sets and then permits the application of the domain knowledge to the monitoring of relevant relations as they occur in evolving situations. SAWA includes tools for developing ontologies in OWL and rules in SWRL and provides runtime components for collecting event data, storing and querying the data, monitoring relevant relations and viewing the results through a graphical user interface. An application of SAWA to a scenario from the domain of supply logistics is also presented.