J. Greg Trafton

Situation awareness recovery.

Objective: We describe a novel concept, situation awareness recovery (SAR), and we identify perceptual and cognitive processes that characterize SAR. Background: Situation awareness (SA) is typically described in terms of perceiving relevant elements of the environment, comprehending how those elements are integrated into a meaningful whole, and projecting that meaning into the future. Yet SA fluctuates during the time course of a task, making it important to understand the process by which SA is recovered after it is degraded. Method: We investigated SAR using different types of interruptions to degrade SA. In Experiment 1, participants watched short videos of an operator performing a supervisory control task, and then the participants were either interrupted or not interrupted, after which SA was assessed using a questionnaire. In Experiment 2, participants performed a supervisory control task in which they guided vehicles to their respective targets and either experienced an interruption, during which they performed a visual search task in a different panel, or were not interrupted. Results: The SAR processes we identified included shorter fixation durations, increased number of objects scanned, longer resumption lags, and a greater likelihood of refixating on objects that were previously looked at. Conclusions: We interpret these findings in terms of the memory-for-goals model, which suggests that SAR consists of increased scanning in order to compensate for decay, and previously viewed cues act as associative primes that reactivate memory traces of goals and plans.

High assurance human-centric decision systems

Many future decision support systems will be human-centric, i.e., require substantial human oversight and control. Because these systems often provide critical services, high assurance will be needed that they satisfy their requirements. How to develop “high assurance human-centric decision systems” is unknown: while significant research has been conducted in areas such as agents, cognitive science, and formal methods, how to apply and integrate the design principles and disparate models in each area is unclear. This paper proposes a novel process for developing human-centric decision systems where AI (artificial intelligence) methods-namely, cognitive models to predict human behavior and agents to assist the human-are used to achieve adequate system performance, and software engineering methods, namely, formal modeling and analysis, to obtain high assurance. To support this process, the paper introduces a software engineering technique-formal model synthesis from scenarios-and two AI techniques-a model for predicting human overload and user model synthesis from participant studies data. To illustrate the process and techniques, the paper describes a decision system controlling unmanned air vehicles.

Fighting fires with human robot teams

This video submission demonstrates cooperative human-robot firefighting. A human team leader guides the robot to the fire using a combination of speech and gesture.