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Bayesian networks for spatial learning: a workflow on using limited survey data for intelligent learning in spatial agent-based models
GeoInformatica ( IF 2 ) Pub Date : 2019-04-26 , DOI: 10.1007/s10707-019-00347-0
Shaheen A. Abdulkareem , Yaseen T. Mustafa , Ellen-Wien Augustijn , Tatiana Filatova

Machine learning (ML) algorithms steer agent decisions in agent-based models (ABMs), serving as a vehicle for implementing behaviour changes during simulation runs. However, when training an ML algorithm, obtaining large sets of micro-level human behaviour data is often problematic. Information on human behaviour is often collected via surveys of relatively small sample sizes. This paper presents a methodology for training a learning algorithm to guide agent behaviour in a spatial ABM using a limited survey data sample. We apply different implementation strategies using survey data and Bayesian networks (BNs). By being grounded in probabilistic directed graphical models, BNs stand out among other learning algorithms in that they can be based on expert knowledge and/or known datasets. This paper presents four alternative implementations of data-driven BNs to support agent decisions in a spatial ABM. We differentiate between training BNs prior to, or during the simulation runs, using only survey data or a combination of survey data and expert knowledge. The four different implementations are then illustrated using a spatial ABM of cholera diffusion for Kumasi, Ghana. The results indicate that a balance between expert knowledge and survey data provides the best control over the learning process of the agents and produces the most realistic agent behaviour.

中文翻译:

贝叶斯空间学习网络:在基于空间代理的模型中使用有限调查数据进行智能学习的工作流程

机器学习(ML)算法在基于代理的模型(ABM)中引导代理决策,充当在模拟运行期间实现行为更改的工具。但是,在训练ML算法时,获得大量的微观人类行为数据集通常是有问题的。有关人类行为的信息通常是通过对较小样本量的调查收集的。本文提出了一种使用有限的调查数据样本来训练学习算法以指导空间ABM中的代理行为的方法。我们使用调查数据和贝叶斯网络(BN)应用不同的实施策略。通过以概率为导向的图形模型为基础,BN在其他学习算法中脱颖而出,因为它们可以基于专家知识和/或已知数据集。本文介绍了数据驱动BN的四种替代实现,以支持空间ABM中的代理决策。我们仅使用调查数据或调查数据与专家知识的组合来区分模拟运行之前或期间的培训BN。然后使用加纳库玛西的霍乱扩散的空间ABM说明了四种不同的实现方式。结果表明,专家知识和调查数据之间的平衡提供了对代理商学习过程的最佳控制,并产生了最现实的代理商行为。然后使用加纳库玛西的霍乱扩散的空间ABM说明了四种不同的实现方式。结果表明,专家知识和调查数据之间的平衡提供了对代理商学习过程的最佳控制,并产生了最现实的代理商行为。然后使用加纳库玛西的霍乱扩散的空间ABM说明了四种不同的实现方式。结果表明,专家知识和调查数据之间的平衡提供了对代理商学习过程的最佳控制,并产生了最现实的代理商行为。
更新日期:2019-04-26
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