Distributed Resource Allocation for Human-Autonomy Teaming under Coupled Constraints

This paper studies the optimal resource allocation problem within a multi-agent network composed of both autonomous agents and humans. The main challenge lies in the globally coupled constraints that link the decisions of autonomous agents with those of humans. To address this, we propose a reformulation that transforms these coupled constraints into decoupled local constraints defined over the system's communication graph. Building on this reformulation and incorporating a human response model that captures human-robot interactions while accounting for individual preferences and biases, we develop a fully distributed algorithm. This algorithm guides the states of the autonomous agents to equilibrium points which, when combined with the human responses, yield a globally optimal resource allocation. We provide both theoretical analysis and numerical simulations to validate the effectiveness of the proposed approach.