  This paper addresses the challenge of enabling a single robot to effectively
assist multiple humans in decision-making for task planning domains. We
introduce a comprehensive framework designed to enhance overall team
performance by considering both human expertise in making the optimal decisions
and robot influence on human decision-making. Our model integrates these
factors seamlessly within the task-planning domain, formulating the problem as
a partially observable Markov decision process (POMDP) while treating expertise
and influence as unobservable components of the system state. To solve for the
robot's actions in such systems, we propose an efficient Attention-Switching
policy. This policy capitalizes on the inherent structure of such systems,
solving multiple smaller POMDPs to generate heuristics for prioritizing
interactions with different human teammates, thereby reducing the state space
and improving scalability. Our empirical results on a simulated kit fulfillment
task demonstrate improved team performance when the robot's policy accounts for
both expertise and influence. This research represents a significant step
forward in the field of adaptive robot assistance, paving the way for
integration into cost-effective small and mid-scale industries, where
substantial investments in robotic infrastructure may not be economically
viable.
