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Urban Structure Generalization in Multi-Agent Process by Use of Reactional Agents

机译：基于Agent的多Agent过程中城市结构的一般化

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This article proposes an improvement of automated cartographic generalization using multi-agent sytems in urban areas. Indeed the AGENT model, whose robustness has been tested and approved through the European project AGENT, gives very good results in generalizing dense urban areas by means of enlargement, removal and displacement of buildings. But this model does not tackle the question of including particular structures like building alignments in the process, which is a crucial issue. The problem is that integrating such structures does not fit into the accurate top-down hierarchy of urban agents. In order to face this problem, we propose to partly re-engineer the model by introducing the concept of reactional agents whose behavior is very different from hierarchical agents of the original model as they use bottom-up activation. In this view, urban alignment is considered to be a reactional agent activated only by its inner buildings, which generalizes the aligned buildings together into one entire structure. Associating reactional alignment behavior with new generalization actions on alignments significantly improves the model and gives better results in dense urban areas. Moreover, the idea could probably be used for other applications.
机译：本文提出了一种在城市地区使用多智能体系统对自动制图综合进行改进的方法。实际上，通过欧洲项目AGENT测试并批准了其健壮性的AGENT模型，在通过扩展，拆除和置换建筑物来概括密集的城市区域方面，给出了很好的结果。但是，此模型无法解决在此过程中包括建筑物对齐等特殊结构的问题，这是至关重要的问题。问题在于，将这样的结构集成到城市代理商的精确的自上而下的层次结构中是不合适的。为了解决这个问题，我们建议通过引入反应代理的概念来部分重新设计模型，该模型的行为与原始模型的分层代理非常不同，因为它们使用自下而上的激活。按照这种观点，城市路线被认为是仅由其内部建筑物激活的反应代理，它将内部对齐的建筑物概括为一个整体结构。将反应路线行为与路线上的新泛化动作相关联可显着改善模型，并在人口稠密的城市地区提供更好的结果。而且，该想法可能会用于其他应用程序。

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