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Frontiers of Information Technology & Electronic Engineering

ISSN 2095-9184

Frontiers of Information Technology & Electronic Engineering  2017, Vol. 18 Issue (1): 139-148   https://doi.org/10.1631/FITEE.1601608
  本期目录
基于面向任务的协同特征向量的联盟形成算法
方浩1,2(),卢少磊1,2(),陈杰1,2(),陈文颉1,2()
1. 北京理工大学自动化学院
2. 北京理工大学复杂系统智能控制与决策重点实验室
Coalition formation based on a task-oriented collaborative ability vector
Hao FANG1,2(),Shao-lei LU1,2(),Jie CHEN1,2(),Wen-jie CHEN1,2()
1. School of Automation, Beijing Institute of Technology, Beijing 100081, China
2. State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing 100081, China
 全文: PDF(491 KB)  
摘要:

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。

Abstract

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.

Key wordsCollaborative vector    Task allocation    Multi-agent system    Coalition formation    Artificial intelligence
收稿日期: 2016-10-09      出版日期: 2017-02-27
通讯作者: 方浩     E-mail: fangh@bit.edu.cn;lu_shaolei@126.com;chenjie@bit.edu.cn;chen.wenjie@163.com
Corresponding Author(s): Hao FANG   
 引用本文:   
方浩,卢少磊,陈杰,陈文颉. 基于面向任务的协同特征向量的联盟形成算法[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 139-148.
Hao FANG,Shao-lei LU,Jie CHEN,Wen-jie CHEN. Coalition formation based on a task-oriented collaborative ability vector. Front. Inform. Technol. Electron. Eng, 2017, 18(1): 139-148.
 链接本文:  
https://academic.hep.com.cn/fitee/CN/10.1631/FITEE.1601608
https://academic.hep.com.cn/fitee/CN/Y2017/V18/I1/139
1 An, B., Shen, Z.Q., Miao, C.Y., , 2007. Algorithms for transitive dependence-based coalition formation. IEEE Trans. Ind. Inform., 3(3):234–245.
2 Auer, S., Heitzig, J., Kornek, U., , 2015. The dynamics of coalition formation on complex networks. Sci. Rep., 5, Article 13386.
3 Bonabeau, E., Sobkowski, A., Theraulaz, G., , 1997. Adaptive task allocation inspired by a model of division of labor in social insects. Proc. Biocomputing and Emergent Computation, p.36–45.
4 Diao, X.H., Fang, Y.W., Xiao, B.S., , 2014. Task allocation in cooperative air combat based on multiagent coalition. J. Beijing Univ. Aeronaut. Astronaut., 40(9):1268–1275 (in Chinese).
5 Du, J.P., Zhou, L., Qu, P., , 2010. Task allocation in multi-agent systems with swarm intelligence of social insects. 6th Int. Conf. on Natural Computation, p.4322–4326.
6 Gensollen, N., Becker, M., Gauthier, V., , 2015. Coalition formation algorithm of prosumers in a smart grid environment. IEEE Int. Conf. on Communications, p.5896–5902.
7 Haque, M.A., Egerstedt, M., 2009. Coalition formation in multi-agent systems based on bottlenose dolphin alliances. American Control Conf., p.3280–3285.
8 Haque, M., Egerstedt, M., Rahmani, A., 2013. Multilevel coalition formation strategy for suppression of enemy air defenses missions. J. Aerosp. Inform. Syst., 10(6):287–296.
9 Ketchpel, S., 1994. Forming coalitions in the face of uncertain rewards. AAAI National Conf. on Artificial Intelligence, p.414–419.
10 Li, D.Y., Du, Y., 2014. Artificial Intelligence with Uncertainty (2nd Ed.). National Defence Industry Press, Beijing (in Chinese).
11 Lu, S.L., Fang, H., 2016. An improved distributed coalition formation algorithm in MAS. Contr. Dec. , in press.
12 Pan, Y.H., 2016. Heading toward artificial intelligence 2.0. Engineering, 2(4):409–413.
13 Saaty, T.L., 1990. How to make a decision: the analytic hierarchy process. Eur. J. Oper. Res., 48(1):9–26.
14 Saaty, T.L., 2008. Decision making with the analytic hierarchy process. Int. J. Serv. Sci. , 1(1):83–98.
15 Sandhlom, T.W., Lesser, V.R.T., 1997. Coalitions among computationally bounded agents. Artif. Intell., 94(1):99–137.
16 Sellner, B., Heger, F.W., Hiatt, L.M., , 2006. Coordinated multi-agent teams and sliding autonomy for large-scale assembly. Proc. IEEE, 94(7):1425–1444.
17 Shehory, O., Kraus, S., 1996. A kernel-oriented model for coalition-formation in general environments: implementation and results. AAAI/IAAI, p.134-140.
18 Shehory, O., Kraus, S., 1998. Methods for task allocation via agent coalition formation. Artif. Intell. , 101(1):165–200.
19 Sichman, J.S., Conte, R., Demazeau, Y., , 1998. A social reasoning mechanism based on dependence networks. Proc. 11th European Conf. on Artificial Intelligence, p.416–420.
20 Whitbrook, A., Meng, Q.G., Chung, P.W.H., 2015. A novel distributed scheduling algorithm for time-critical multiagent systems. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, p.6451–6458.
21 Ye, D.Y., Zhang, M.J., Sutanto, D., 2015. Decentralised dispatch of distributed energy resources in smart grids via multi-agent coalition formation. J. Parall. Distr. Comput., 83:30–43.
22 Zhao, W.Q., Meng, Q.G., Chung, P.W.H., 2016. A heuristic distributed task allocation method for multivehicle multitask problems and its application to search and rescue scenario. IEEE Trans. Cybern. , 46(4):902–915.
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