Coalition formation based on a task-oriented collaborative ability vector  

基于面向任务的协同特征向量的联盟形成算法（英文）

作　　者：Hao FANG Shao-lei LU Jie CHEN Wen-jie CHEN 

机构地区：[1]School of Automation,Beijing Institute of Technology [2]State Key Laboratory of Intelligent Control and Decision of Complex Systems

出　　处：《Frontiers of Information Technology & Electronic Engineering》2017年第1期139-148,共10页信息与电子工程前沿（英文版）

基　　金：supported by the National Natural Science Foundation of China(Nos.61573062,61120106010,and 61321002);the Beijing Outstanding Ph.D. Program Mentor(No.20131000704);the Beijing Advanced Innovation Center for Intelligent Robots and Systems(Beijing Institute of Technology)

摘　　要：Coalition formation is an important coordination problem in multi-agent systems, and a proper description of collaborative abilities for agents is the basic and key precondition in handling this problem. In this paper, a model of task-oriented collaborative abilities is established, where five task-oriented abilities are extracted to form a collaborative ability vector. A task demand vector is also described. In addition, a method of coalition formation with stochastic mechanism is proposed to reduce excessive competitions. An artificial intelligent algorithm is proposed to compensate for the difference between the expected and actual task requirements, which could improve the cognitive capabilities of agents for human commands. Simulations show the effectiveness of the proposed model and the distributed artificial intelligent algorithm.联盟形成是多智能体系统中一个重要的协同问题,对智能体的协同能力进行适当的描述是处理这个问题的一个基本且必要的前提。这篇文章对智能体的协同能力进行了建模,该模型由五个影响因素构成。同时,对任务需求向量进行了描述。提了一种随机机制以减少联盟形成过程中的过度竞争。此外,为了减少任务需求和实际任务需求之间的差距,提出了一种人工智能方法,该方法可以提高多智能体对人类指令的认知。实验结果显示了该模型及分布式人工智能方法的有效性。

关 键 词：Collaborative vector Task allocation Multi-agent system Coalition formation Artificial intelligence 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]