A game theoretic queueing approach to self-reflection in decentralized human-robot interaction systems

This paper presents a queueing model that addresses robot self-assessment in human-robot-interaction systems. We build the model based on a game theoretic queueing approach, and analyze four issues: 1) individual differences in operator skills/capabilities, 2) differences in difficulty of presenting tasks, 3) trade-off between human interaction and performance and 4) the impact of task heterogeneity in the optimal service decision-making and system performance. The subsequent analytical and numerical exploration helps understand the way the decentralized decision-making scheme is affected by various service environments