A field-based versus a protocol-based approach for adaptive task assignment

被引:12
|
作者
Weyns, Danny [1 ]
Boucke, Nelis [1 ]
Holvoet, Tom [1 ]
机构
[1] Katholieke Univ Leuven, Louvain, Belgium
关键词
task assignment; gradient fields; extended contract net protocol; automatic guided vehicles; AGN;
D O I
10.1007/s10458-008-9037-x
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Task assignment in multi-agent systems is a complex coordination problem, in particular in systems that are subject to dynamic and changing operating conditions. To enable agents to deal with dynamism and change, adaptive task assignment approaches are needed. In this paper, we study two approaches for adaptive task assignment that are characteristic for two classical families of task assignment approaches. FiTA is a field-based approach in which tasks emit fields in the environment that guide idle agents to tasks. DynCNET is a protocol-based approach that extends Standard Contract Net (CNET). In DynCNET, agents use explicit negotiation to assign tasks. We compare both approaches in a simulation of an industrial automated transportation system. Our experiences show that: (1) the performance of DynCNET and FiTA are similar, while both outperform CNET; (2) the complexity to engineer DynCNET is similar to FiTA but much more complex than CNET; (3) whereas task assignment with FiTA is an emergent solution, DynCNET specifies the interaction among agents explicitly allowing engineers to reason on the assignment of tasks, (4) FiTA is inherently robust to message loss while DynCNET requires substantial additional support. The tradeoff between (3) and (4) is an important criteria for the selection of an adaptive task assignment approach in practice.
引用
收藏
页码:288 / 319
页数:32
相关论文
共 50 条
  • [31] Epidemiology-based Task Assignment Algorithm for Distributed Systems
    Brahmbhatt, Parth
    Camorlinga, Sergio G.
    COMPLEX ADAPTIVE SYSTEMS, 2016, 95 : 428 - 435
  • [32] Task Assignment of UAV Swarm Based on Wolf Pack Algorithm
    Lu, Yingtong
    Ma, Yaofei
    Wang, Jiangyun
    Han, Liang
    APPLIED SCIENCES-BASEL, 2020, 10 (23): : 1 - 17
  • [33] QITA: Quality Inference Based Task Assignment in Mobile Crowdsensing
    Liu, Chenlin
    Gao, Xiaofeng
    Wu, Fan
    Chen, Guihai
    SERVICE-ORIENTED COMPUTING (ICSOC 2018), 2018, 11236 : 363 - 370
  • [34] Task Assignment Algorithm Based on Trust in Volunteer Computing Platforms
    Xu, Ling
    Qiao, Jianzhong
    Lin, Shukuan
    Qi, Ruihua
    INFORMATION, 2019, 10 (07)
  • [35] Cooperative task assignment for UAV based on SA-QCDPSO
    Zhang, Jiandong
    Chen, Yuyang
    Tang, Yueqing
    Wang, Shuo
    Yu, Xiao
    Chen, Guiying
    2020 IEEE 16TH INTERNATIONAL CONFERENCE ON CONTROL & AUTOMATION (ICCA), 2020, : 864 - 869
  • [36] A Task Assignment Method for Sweep Coverage Optimization Based on Crowdsensing
    Wu, Liangguang
    Xiong, Yonghua
    Wu, Min
    He, Yong
    She, Jinhua
    IEEE INTERNET OF THINGS JOURNAL, 2019, 6 (06) : 10686 - 10699
  • [37] An SOM-Based Algorithm with Locking Mechonism for Task Assignment
    Sun, Wei
    Zhang, Fei
    Xue, Min
    Hu, Wenhui
    Li, Long
    2017 IEEE INTERNATIONAL CONFERENCE ON CYBERNETICS AND INTELLIGENT SYSTEMS (CIS) AND IEEE CONFERENCE ON ROBOTICS, AUTOMATION AND MECHATRONICS (RAM), 2017, : 36 - 41
  • [38] A Mobile Crowd Sensing Based Task Assignment in Internet of Things
    George, Lincy M.
    Babu, K. R. Remesh
    IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGICAL TRENDS IN COMPUTING, COMMUNICATIONS AND ELECTRICAL ENGINEERING (ICETT), 2016,
  • [39] PHM-Based Multi-UAV Task Assignment
    de Medeiros, Ivo Paixao
    Rodrigues, Leonardo Ramos
    Shiguemori, Elcio Hideiti
    Santos, Rafael
    Nascimento Junior, Cairo Lucio
    2014 8TH ANNUAL IEEE SYSTEMS CONFERENCE (SYSCON), 2014, : 42 - 49
  • [40] Task Assignment of UAV Swarms Based on Deep Reinforcement Learning
    Liu, Bo
    Wang, Shulei
    Li, Qinghua
    Zhao, Xinyang
    Pan, Yunqing
    Wang, Changhong
    DRONES, 2023, 7 (05)