KEYWORDS: Cognitive modeling, Decision support systems, Computer architecture, Systems modeling, Human-machine interfaces, Control systems, Data modeling, Analytical research, Process modeling, Visualization
Decision-making is strongly shaped and influenced by the work context in which decisions are embedded. This suggests that decision support needs to be anchored by a model (implicit or explicit) of the work process, in contrast to traditional approaches that anchor decision support to either context free decision models (e.g., utility theory) or to detailed models of the external (e.g., battlespace) environment. An architecture for cognitively-based, work centered decision support called the Work-centered Informediary Layer (WIL) is presented. WIL separates decision support into three overall processes that build and dynamically maintain an explicit context model, use the context model to identify opportunities for decision support and tailor generic decision-support strategies to the current context and offer them to the system-user/decision-maker. The generic decision support strategies include such things as activity/attention aiding, decision process structuring, work performance support (selective, contextual automation), explanation/ elaboration, infosphere data retrieval, and what if/action-projection and visualization. A WIL-based application is a work-centered decision support layer that provides active support without intent inferencing, and that is cognitively based without requiring classical cognitive task analyses. Example WIL applications are detailed and discussed.
KEYWORDS: Cognitive modeling, Systems modeling, Sensors, Performance modeling, Eye models, Control systems, Cognition, Visual process modeling, Environmental sensing, Data modeling
There are a variety of problems occurring over the life cycle of an Integrated Command Environment (ICE) that can be addressed with a common approach, i.e. cognitive modeling. Cognitive models are a special type of intelligent agent constructed not only to behave in an intelligent fashion, but also to simulate human behavior. When simulating human behavior, a wide range of simulation realism is possible. A good balance between realism vs. minimum modeling effort and the most efficient CPU time usage should be sought when developing models for a particular purpose and domain. Thus, the level of perceptual and motor detail represented in a cognitive model should be scaled based on the ICE activity being supported. This paper discusses the level of realism needed at different stages in the life cycle of an ICE, and presents improvements to the existing COGNET cognitive modeling framework that support ICE modeling.
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