Optimizing Delegation in Collaborative Human-AI Hybrid Teams
Andrew Fuchs Andrea Passarella Marco Conti
When humans and autonomous systems operate together as what we refer to as a
hybrid team, we of course wish to ensure the team operates successfully and
effectively. We refer to team members as agents. In our proposed framework, we
address the case of hybrid teams in which, at any time, only one team member
(the control agent) is authorized to act as control for the team. To determine
the best selection of a control agent, we propose the addition of an AI manager
(via Reinforcement Learning) which learns as an outside observer of the team.
The manager learns a model of behavior linking observations of agent
performance and the environment/world the team is operating in, and from these
observations makes the most desirable selection of a control agent. We restrict
the manager task by introducing a set of constraints. The manager constraints
indicate acceptable team operation, so a violation occurs if the team enters a
condition which is unacceptable and requires manager intervention. To ensure
minimal added complexity or potential inefficiency for the team, the manager
should attempt to minimize the number of times the team reaches a constraint
violation and requires subsequent manager intervention. Therefore our manager
is optimizing its selection of authorized agents to boost overall team
performance while minimizing the frequency of manager intervention. We
demonstrate our manager performance in a simulated driving scenario
representing the case of a hybrid team of agents composed of a human driver and
autonomous driving system. We perform experiments for our driving scenario with
interfering vehicles, indicating the need for collision avoidance and proper
speed control. Our results indicate a positive impact of our manager, with some
cases resulting in increased team performance up to ~187% that of the best solo
agent performance.