Optimizing Risk-averse Human-AI Hybrid Teams
Andrew Fuchs Andrea Passarella Marco Conti
We anticipate increased instances of humans and AI systems working together
in what we refer to as a hybrid team. The increase in collaboration is expected
as AI systems gain proficiency and their adoption becomes more widespread.
However, their behavior is not error-free, making hybrid teams a very suitable
solution. As such, we consider methods for improving performance for these
teams of humans and AI systems. For hybrid teams, we will refer to both the
humans and AI systems as agents. To improve team performance over that seen for
agents operating individually, we propose a manager which learns, through a
standard Reinforcement Learning scheme, how to best delegate, over time, the
responsibility of taking a decision to any of the agents. We further guide the
manager's learning so they also minimize how many changes in delegation are
made resulting from undesirable team behavior. We demonstrate the optimality of
our manager's performance in several grid environments which include failure
states which terminate an episode and should be avoided. We perform our
experiments with teams of agents with varying degrees of acceptable risk, in
the form of proximity to a failure state, and measure the manager's ability to
make effective delegation decisions with respect to its own risk-based
constraints, then compare these to the optimal decisions. Our results show our
manager can successfully learn desirable delegations which result in team paths
near/exactly optimal with respect to path length and number of delegations.