Abstraction of agent interaction processes: Towards large-scale multi-agent models
Abstraction of agent interaction processes: Towards large-scale multi-agent models
复制标题
代理交互过程的抽象：迈向大规模多代理模型
DOI：
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复制
发表时间：
2013
期刊：
影响因子：
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通讯作者：
C. Jacob
中科院分区：
文献类型：
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作者：
A. S. Shirazi;S. Mammen;C. Jacob
在基于代理的模拟中，通常大量的交互会带来相当大的计算成本。在这篇文章中，我们提出了一种方法，以减少在运行时重复出现的行为模式的基础上的交互的数量。我们采用机器学习技术来抽象代理群体的行为，以降低计算复杂性，同时保持基于代理的模型的固有灵活性。学习到的抽象，它隐含了底层模型代理的交互，不断地测试它们的有效性：毕竟，系统的动态可能会随着时间的推移而改变，以至于以前学习到的模式不会重新出现。因此，一个无效的抽象被再次从系统中删除。在整个仿真过程中，抽象的创建和删除都在继续，以确保对系统动态的充分适应。基于生物代理的仿真实验结果表明，我们所提出的方法可以成功地减少在模拟过程中的计算复杂度，同时保持任意交互的自由。
The typically large numbers of interactions in agent-based simulations come at considerable computational costs. In this article, we present an approach to reduce the number of interactions based on behavioural patterns that recur during runtime. We employ machine learning techniques to abstract the behaviour of groups of agents to cut down computational complexity while preserving the inherent flexibility of agent-based models. The learned abstractions, which subsume the underlying model agents’ interactions, are constantly tested for their validity: after all, the dynamics of a system may change over time to such an extent that previously learned patterns would not reoccur. An invalid abstraction is, therefore, removed again from the system. The creation and removal of abstractions continues throughout the course of a simulation in order to ensure an adequate adaptation to the system dynamics. Experimental results on biological agent-based simulations show that our proposed approach can successfully reduce the computational complexity during the simulation while maintaining the freedom of arbitrary interactions.
DOI：
10.1046/j.1432-1327.2000.01197.x
发表时间：
2000-03-01
期刊：
EUROPEAN JOURNAL OF BIOCHEMISTRY
影响因子：
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作者：
Kholodenko, BN
通讯作者：
Kholodenko, BN
DOI：
10.1073/pnas.93.19.10078
发表时间：
1996-09-17
影响因子：
11.1
作者：
Huang, CYF;Ferrell, JE
通讯作者：
Ferrell, JE