On-line situation awareness and knowledge acquisition

Janet Burge; Paul Gonsalves; Catherine Call

doi:10.1117/12.606032

25 May 2005 On-line situation awareness and knowledge acquisition

Janet Burge, Paul Gonsalves, Catherine Call

Proceedings Volume 5809, Signal Processing, Sensor Fusion, and Target Recognition XIV; (2005) https://doi.org/10.1117/12.606032
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

In dynamic environments (e.g. an Air Operations Center (AOC)), effective decision-making is highly dependent on situation awareness (SA). SA is formally defined as a person's "perception of the elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future". Belief networks (BNs) are an ideal tool for modeling and meeting the requirements of tactical situation awareness. BNs emulate a skilled human's information fusion and reasoning process in a multi-task environment in the presence of uncertainty. While belief networks offer significant advantages for SA, a key drawback to their use is the daunting issue of how the requisite knowledge is captured or elicited to both build the network and populate the Conditional Probability Tables (CPTs). To address this issue, we have built the Situation Awareness and Knowledge Acquisition (OSAKA) system. This system consists of two parts: development of a library of BN components that can be combined to describe different air operation situations and enhancing our BNet:Builder© toolkit to learn CPT values for these components based on data obtained from running an external simulation. This allows the initial CPT values obtained from our Subject Matter Expert to be tuned based on what actually happens. The ability to tune the network over time can aid in supplying information not initially available when constructing the network and to help ensure that it continues to provide current, useful, information.

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Janet Burge, Paul Gonsalves, and Catherine Call "On-line situation awareness and knowledge acquisition", Proc. SPIE 5809, Signal Processing, Sensor Fusion, and Target Recognition XIV, (25 May 2005); https://doi.org/10.1117/12.606032

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KEYWORDS

Expectation maximization algorithms

Knowledge acquisition

Cognitive modeling

Data modeling

Injuries

Network architectures

Algorithm development

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