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4.6.2 Intelligent Agent Saves Lives
Author(s) -
Steinberg Richard K.
Publication year - 2003
Publication title -
incose international symposium
Language(s) - English
Resource type - Journals
ISSN - 2334-5837
DOI - 10.1002/j.2334-5837.2003.tb02670.x
Subject(s) - task (project management) , computer science , focus (optics) , control (management) , operator (biology) , human error , action (physics) , preference , emergency management , user interface , interface (matter) , intelligent agent , intelligent decision support system , command and control , human–computer interaction , risk analysis (engineering) , operations research , artificial intelligence , reliability engineering , engineering , systems engineering , optics , bubble , repressor , law , chemistry , maximum bubble pressure method , microeconomics , operating system , telecommunications , biochemistry , quantum mechanics , parallel computing , political science , transcription factor , medicine , physics , economics , gene
Designing human computer interfaces for rapid command and control decision‐making displays has unique challenges. Displays for Air Traffic Control, Military Operations, and Emergency Management require an interface that optimizes performance while minimizing errors. In addition to performing the emergency functions, maintaining operational readiness of equipment is a task where errors and time delays could cause loss of human life. Interaction efficiency is critical to avoid operator fatigue and minimize operator error rates that could cost lives. In emerging emergency management systems and concepts, the user typically acts as a manager by exception while the majority of system activity is computer automated. While direct interaction of the user with the system is minimal, an inaccurate action by the user can have catastrophic consequences. There is clearly a need for decision aiding systems to help focus users on the most important information. This paper describes a case study that demonstrates the use of a human engineered intelligent agent. The results of this effort suggest as much as a 40% reduction in casualty risks while using the intelligent agent and strong positive preference ratings by all operators who used it.