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Publication Details

Interactive Execution Monitoring of Agent Teams

by Wilkins, D. E., Lee, T. and Berry, P.

Journal of Artificial Intelligence Research, vol. 18, pp. 217-261, March 2003.

Note: HTML Version at http://www.ai.sri.com/~wilkins/papers/jair-2003/

Abstract

There is an increasing need for automated support for humans monitoring the activity of distributed teams of cooperating agents, both human and machine. We characterize the domain-independent challenges posed by this problem, and describe how properties of domains influence the challenges and their solutions. We will concentrate on dynamic, data-rich domains where humans are ultimately responsible for team behavior. Thus, the automated aid should interactively support effective and timely decision making by the human. We present a domain-independent categorization of the types of alerts a plan-based monitoring system might issue to a user, where each type generally requires different monitoring techniques. We describe a monitoring framework for integrating many domain-specific and task-specific monitoring techniques and then using the concept of {value of an alert} to avoid operator overload.

We use this framework to describe an execution monitoring approach we have used to implement Execution Assistants (EAs) in two different dynamic, data-rich, real-world domains to assist a human in monitoring team behavior. One domain (Army small unit operations) has hundreds of mobile, geographically distributed agents, a combination of humans, robots, and vehicles. The other domain (teams of unmanned ground and air vehicles) has a handful of cooperating robots. Both domains involve unpredictable adversaries in the vicinity. Our approach customizes monitoring behavior for each specific task, plan, and situation, as well as for user preferences. Our EAs alert the human controller when reported events threaten plan execution or physically threaten team members. Alerts were generated in a timely manner without inundating the user with too many alerts (less than 10% of alerts are unwanted, as judged by domain experts).

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Associated Projects

UCAV

Intelligent Reactive Control for Distributed UCAV Networks

SUO-PDA

Planning and Decision Aids for Small Unit Operations

We developed a planning and decision aid (PDA) for the DARPA Small Unit Operations (SUO) program, to show the feasibility of using advanced planning technologies in SUO. The PDA monitors the execution of machine-understandable plans, using events as they are realistically reported in the battlespace, and alerts the user when the situation requires his attention.

TEAMBOTICA

TEAMBOTICA: A Robotic framework for integrated teaming, tasking, networking and control

Teambotica is a research initiative to develop the computational framework necessary for intelligent, autonomous teams of robots to operate in hostile environments.

AIC Personnel

Name Title E-mail
Berry, Pauline M Alumnus
Lee, Thomas (Tom) J Senior Research Engineer
Wilkins, David E Senior Computer Scientist

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