Systems, Man & Cybernetics - October 2015 - 15

parameters for team performance, discusses the self-*
properties of ACSs, proposes a general way to conduct AC,
and verifies the benefits of AC by simulations with a general model. Taking the proposed AC approach as a foundation, effective future work in AC can be revealed as
follows. We need to:
◆ collect dynamic qualifications of each agent and
dynamic requirements of each role by conducting
effective AE
◆ consider another role range vector U(U[j] = g.e.B
[j].q.u, U[j] ! N, 0 # j # n - 1) in the process of AC
◆ investigate how to evaluate and form the role
weight vector W according to the dynamic situation
of a group
◆ select appropriate qualification values to extinguish
those unqualified agents before role assignment
◆ determine when a new team state is obtained and
when role reassignment is conducted
◆ adjust the agent performance model to reflect more
practical scenarios, including Definition 23
◆ introduce cost in the model and investigate the benefits of AC [10]
◆ design and implement a role engine to support AC
including the rules and policies that make internal
changes by reflecting the external environments.

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challenges," ACM Trans. Autonomous Adaptive Syst., vol. 4, no. 2, pp. 14.1-14.42, May

About the Author
Haibin Zhu (haibinz@nipissingu.ca) earned his B.S.
degree in computer engineering from the Institute of Engineering and Technology, Zhengzhou, China, (1983) and his
M.S. (1988) and Ph.D. (1997) degrees in computer science
from the National University of Defense Technology
(NUDT), Changsha, China. He is a full professor in the
Department of Computer Science and Mathematics, founder and director of Collaborative Systems Laboratory,
Nipissing University, Canada, and a visiting professor of
the Department of Control Science and Engineering, Nanjing University, China. He was a visiting professor and a
special lecturer in the College of Computing Sciences,
New Jersey Institute of Technology, Newark (1999-2002),
and a lecturer, associate professor, and full professor at
NUDT (1988-2000). He has published more than 150 research papers, five books, and four book chapters on
object-oriented programming, distributed systems, collaborative systems, and computer architecture. He has served
as cochair of the Technical Committee of Distributed Intelligent Systems of the IEEE Systems, Man, and Cybernetics
Society and is an associate editor of IEEE Systems, Man,
& Cybernetics Magazine.

2009.
[12] Y. Sheng, H. Zhu, X. Zhou, and W. Hu, "Effective approaches to adaptive collaboration via dynamic role assignment," IEEE Trans. Syst., Man, Cybern. Syst., vol. 46, no. 1,
pp. 76-92, Jan. 2016.
[13] S. van Splunter, N. J. E. Wijngaards, and F. M. T. Brazier, "Structuring agents for
adaptation," in Adaptive Agents and Multi-Agent Systems, LNAI 2636, E. Alonso, D.
Kudenko, and D. Kazakov Eds., Berlin: Springer-Verlag, 2003, pp. 174-86.
[14] D. Weyns, S. Malek, and J. Andersson, "FORMS: Unifying reference model for formal specification of distributed systems," ACM Trans. Autonomous Adaptive Syst., vol.
7, no. 1, pp. 8:1-8:61, Apr. 2012.
[15] H. Zhu, "Fundamental issues in the design of a role engine," in IEEE Proc. Int.
Symp. Collaborative Technologies and Syst., Irvine, CA, 2008, pp. 399-407.
[16] H. Zhu, "Role-based autonomic systems," Int. J. Software Sci. Computational
Intell., vol. 2, no. 3, pp. 32-51, July 2010.
[17] H. Zhu, L. Feng, and R. Pickering, "Agent evaluation in distributed adaptive systems," IEEE Int. Conf. Syst., Man, Cybern., Manchester, U.K., 2013, pp.752-757.
[18] H. Zhu, M. Hou, and M. C. Zhou, "Adaptive collaboration based on the E-CARGO
model," Int. J. Agent Technologies Syst., vol. 4, no. 1, pp. 59-76, Jan.-Mar. 2012.
[19] H. Zhu and M. Zhou, Object-Oriented Programming with C++: A Project-Based
Approach, Beijing, China: Tsinghua Univ. Press, 2006.
[20] H. Zhu, M. C. Zhou, and R. Alkins, "Group role assignment via a Kuhn-Munkres
algorithm-based solution," IEEE Trans. Syst., Man, Cybern. A, vol. 42, no. 3, pp.
739-750, 2012.
[21] H. Zhu and M. Zhou, "Role-based collaboration and its kernel mechanisms," IEEE

References

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Multi-Agent Systems: Adaptation and Multi-Agent Learning, LNAI 2636,

O c tob e r 2015

IEEE SyStEmS, man, & CybErnEtICS magazInE

15


http://people.scs.carleton.ca/~soma/

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