In this paper, we propose and analyze a distributed negotiation strategy for a multi-agent, multi-attribute negotiation in which the agents have no information about the utility functions of other agents. We analytically prove that, if the zone of agreement is nonempty and the agents concede up to their reservation utilities, agents generating offers using our offer-generation strategy, namely the sequential projection strategy, will converge to an agreement acceptable to all the agents; the convergence property does not depend on the specific concession strategy. In considering agents’ incentive to concede during the negotiation, we propose and analyze a reactive concession strategy. Through computational experiments, we demonstrate that our distributed negotiation strategy yields performance sufficiently close to the Nash bargaining solution and that our algorithms are robust to potential deviation strategies. Methodologically, our paper advances the state of the art of alternating projection algorithms, in that we establish the convergence for the case of multiple, moving sets (as opposed to two static sets in the current literature). Our paper introduces a new analytical foundation for a broad class of computational group decision and negotiation problems.

This paper presents a queueing model that ad dresses 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 under stand the way the decentralized decision-making scheme is affected by various service environments.

In this paper we explore the effects of service level differentiation on a multi-robot control system. We examine the premise that although long interaction time between robots and operators hurts the efficiency of the system, as it generates longer waiting time for robots, it provides robots with longer neglect time and better performance benefiting the system. In the paper we address the problem of how to choose the optimal service level for an operator in a system through a service level differentiation model. Experimental results comparing system performance for different values of system parameters show that a mixed strategy is a general way to get optimal system performance for a large variety of system parameter settings and in all cases is no worse than a pure strategy.