TAIPE: tactical assistants for interaction planning and execution
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Remote Agent: to boldly go where no AI system has gone before
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The saphira architecture for autonomous mobile robots
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TRIPs: an integrated intelligent problem-solving assistant
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Proceedings of the third annual conference on Autonomous Agents
Evaluating new options in the context of existing plans
Artificial Intelligence
Monitoring deployed agent teams
Proceedings of the fifth international conference on Autonomous agents
Implementing soft real-time agent control
Proceedings of the fifth international conference on Autonomous agents
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An Behavior-based Robotics
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Introspective and elaborative processes in rational agents
Annals of Mathematics and Artificial Intelligence
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Envelopes as a Vehicle for Improving the Efficiency of Plan Execution
Envelopes as a Vehicle for Improving the Efficiency of Plan Execution
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IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Determining the loci of anomalies using minimal causal models
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
A plan-based mission control center for autonomous vehicles
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Monitoring agents using declarative planning
Fundamenta Informaticae
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Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Airlift mission monitoring and dynamic rescheduling
Engineering Applications of Artificial Intelligence
Detecting disagreements in large-scale multi-agent teams
Autonomous Agents and Multi-Agent Systems
Distributed multirobot exploration, mapping, and task allocation
Annals of Mathematics and Artificial Intelligence
A temporal logic-based planning and execution monitoring framework for unmanned aircraft systems
Autonomous Agents and Multi-Agent Systems
A fuzzy-based approach to the value of information in complex military environments
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
Observation of large-scale multi-agent based simulations
MABS'11 Proceedings of the 12th international conference on Multi-Agent-Based Simulation
Monitoring Agents using Declarative Planning
Fundamenta Informaticae - The 1st International Workshop on Knowledge Representation and Approximate Reasoning (KR&AR)
Determining the value of information for collaborative multi-agent planning
Autonomous Agents and Multi-Agent Systems
Curing robot autism: a challenge
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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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).