A multi-responsibility–oriented coalition formation framework for dynamic task allocation in mobile–distributed multi-agent systems. (13th November 2018)

• Record Type: Journal Article
• Authors: Xie, Bing; Gu, Xueqiang; Chen, Jing; Shen, LinCheng
• Is Part Of: International journal of advanced robotic systems. Volume 15:Number 6(2018:Nov./Dec.)
• Publication Date: 2018-11-13
• Subjects: Dynamic task allocation -- multi-responsibility -- Bayesian overlapping coalition game -- overlapping coalition game -- constrained Bayesian coalition game -- self-adaptation -- Bayesian equilibrium; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881418813037
• Languages: English
• ISSNs: 1729-8806

Abstract

In this article, we study a problem of dynamic task allocation with multiple agent responsibilities in distributed multi-agent systems. Agents in the research have two responsibilities, communication and task execution. Movements in agent task execution bring changes to the system network structure, which will affect the communication. Thus, agents need to be autonomous on communication network reconstruction for good performance on task execution. First, we analyze the relationships between the two responsibilities of agents. Then, we design a multi-responsibility–oriented coalition formation framework for dynamic task allocation with two parts, namely, task execution and self-adaptation communication. For the former part, we integrate our formerly proposed algorithm in the framework for task execution coalition formation. For the latter part, we develop a constrained Bayesian overlapping coalition game model to formulate the communication network. A task-allocation efficiency–oriented communication coalition utility function is defined to optimize a coalition structure for the constrained Bayesian overlapping coalition game model. Considering the geographical location dependence between the two responsibilities, we define constrained agent strategies to map agent strategies to potential location choices. Based on the abovementioned design, we propose a distributed location pruning self-adaptive algorithm for the constrained Bayesian overlapping coalition formation. Finally, we test the performance of our framework, multi-responsibility–oriented coalition formation framework, with simulation experiments. Experimental results demonstrate that the multi-responsibility oriented coalition formation framework performs better than the other two distributed algorithms on task completion rate (by over 9.4% and over 65% on average, respectively).