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Proceedings Paper

Combining cognitive engineering and information fusion architectures to build effective joint systems
Author(s): Amy L. Sliva; Joe Gorman; Martin Voshell; James Tittle; Christopher Bowman
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Paper Abstract

The Dual Node Decision Wheels (DNDW) architecture concept was previously described as a novel approach toward integrating analytic and decision-making processes in joint human/automation systems in highly complex sociotechnical settings. In this paper, we extend the DNDW construct with a description of components in this framework, combining structures of the Dual Node Network (DNN) for Information Fusion and Resource Management with extensions on Rasmussen’s Decision Ladder (DL) to provide guidance on constructing information systems that better serve decision-making support requirements. The DNN takes a component-centered approach to system design, decomposing each asset in terms of data inputs and outputs according to their roles and interactions in a fusion network. However, to ensure relevancy to and organizational fitment within command and control (C2) processes, principles from cognitive systems engineering emphasize that system design must take a human-centered systems view, integrating information needs and decision making requirements to drive the architecture design and capabilities of network assets. In the current work, we present an approach for structuring and assessing DNDW systems that uses a unique hybrid DNN top-down system design with a human-centered process design, combining DNN node decomposition with artifacts from cognitive analysis (i.e., system abstraction decomposition models, decision ladders) to provide work domain and task-level insights at different levels in an example intelligence, surveillance, and reconnaissance (ISR) system setting. This DNDW structure will ensure not only that the information fusion technologies and processes are structured effectively, but that the resulting information products will align with the requirements of human decision makers and be adaptable to different work settings .

Paper Details

Date Published: 12 May 2016
PDF: 10 pages
Proc. SPIE 9831, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VII, 98310M (12 May 2016); doi: 10.1117/12.2223926
Show Author Affiliations
Amy L. Sliva, Charles River Analytics, Inc. (United States)
Joe Gorman, Charles River Analytics, Inc. (United States)
Martin Voshell, Charles River Analytics, Inc. (United States)
James Tittle, Charles River Analytics, Inc. (United States)
Christopher Bowman, Data Fusion and Neural Networks, LLC (United States)

Published in SPIE Proceedings Vol. 9831:
Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VII
Michael A. Kolodny; Tien Pham, Editor(s)

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