Interactive Execution Monitoring of Agent Teams

TL;DR

This paper presents a domain-independent framework for interactive execution monitoring of agent teams, supporting human decision-making in dynamic, data-rich environments with real-world applications in military and robotic domains.

Contribution

It introduces a flexible, value-based alerting system and demonstrates its effectiveness in two complex, real-world scenarios involving diverse agent teams.

Findings

Less than 10% of alerts are unwanted, indicating effective alert management.

The framework successfully supports human monitoring in two distinct, dynamic domains.

Customizable monitoring improves timely decision-making and reduces operator overload.

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 percent of alerts are unwanted, as judged by domain experts).