Adaptive learning algorithm of self-organizing teams
Adaptive learning algorithm of self-organizing teams
复制标题
自组织团队的自适应学习算法
DOI：
10.1016/j.eswa.2013.11.008
复制
发表时间：
2014-05
影响因子：
8.5
通讯作者：
Wei Liu
中科院分区：
文献类型：
--
作者：
Jing Li;Chun Ding;Wei Liu
为了提高自组织团队取得良好绩效的能力，提出了一种团队成员自适应学习算法。自组织团队的成员由代理人模拟。在虚拟自组织团队中，代理人根据合作原则调整他们的知识。自适应学习算法的探讨，学习从其他代理以最小的成本，提高自组织团队的性能。在算法中，智能体学习如何行为（选择不同的游戏策略）以及如何行为（选择学习半径）。虚拟团队是根据策略在过去几代中产生更好的质量解决方案的能力自适应地改进。六个基本的实验操作，以证明自适应学习算法的有效性。据发现，自适应学习算法往往会导致代理收敛到最佳的行动，基于代理的不断更新的认知地图的行动如何影响虚拟自组织团队的性能。本文考虑了自组织团队中的关系对现有工作的影响。结果表明，自适应学习算法有利于自组织团队的发展和个体Agent的性能。
In order to improve the ability of achieving good performance in self-organizing teams, this paper presents a self-adaptive learning algorithm for team members. Members of the self-organizing teams are simulated by agents. In the virtual self-organizing team, agents adapt their knowledge according to cooperative principles. The self-adaptive learning algorithm is approached to learn from other agents with minimal costs and improve the performance of the self-organizing team. In the algorithm, agents learn how to behave (choose different game strategies) and how much to think about how to behave (choose the learning radius). The virtual team is self-adaptively improved according to the strategies’ ability of generating better quality solutions in the past generations. Six basic experiments are manipulated to prove the validity of the adaptive learning algorithm. It is found that the adaptive learning algorithm often causes agents to converge to optimal actions, based on agents’ continually updated cognitive maps of how actions influence the performance of the virtual self-organizing team. This paper considered the influence of relationships in self-organizing teams over existing works. It is illustrated that the adaptive learning algorithm is beneficial to both the development of self-organizing teams and the performance of the individual agent.
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DOI：
10.2139/ssrn.180834
发表时间：
1998-07
期刊：
Microeconomic Theory eJournal
影响因子：
--
作者：
Nicolaas J. Vriend
通讯作者：
Nicolaas J. Vriend
合作团队的多目标Q-learning
影响因子：
8.5
作者：
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通讯作者：
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DOI：
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发表时间：
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期刊：
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影响因子：
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2001-06
期刊：
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影响因子：
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通讯作者：
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DOI：
10.1016/j.ins.2010.01.007
发表时间：
2010-05
期刊：
Inf. Sci.
影响因子：
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作者：
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通讯作者：
R. Mallipeddi;S. Mallipeddi;P. Suganthan