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摘要翻译：
自然科学和社会科学中的许多模型都是由一系列相互作用的实体组成的，这些实体的相互作用强度随着距离的增加而减小。这常常导致这些模型中的感兴趣的结构由密集的实体包组成。快速多极子方法是一系列方法，用于帮助计算许多可计算模型，如上文所述。我们提出了一种基于FMM的方法来检测和建模这些系统的密集结构。
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英文标题：
《Building upon Fast Multipole Methods to Detect and Model Organizations》
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作者：
Pierrick Tranouez (LITIS), Antoine Dutot (LITIS)
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最新提交年份：
2009
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
Many models in natural and social sciences are comprised of sets of inter-acting entities whose intensity of interaction decreases with distance. This often leads to structures of interest in these models composed of dense packs of entities. Fast Multipole Methods are a family of methods developed to help with the calculation of a number of computable models such as described above. We propose a method that builds upon FMM to detect and model the dense structures of these systems.
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PDF链接：
https://arxiv.org/pdf/0910.1014
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