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.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.