自组织与人工生命

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摘要翻译：
自组织可以被广义地定义为一个系统显示有序时空模式的能力，这是系统组件之间相互作用的结果。这种过程既是生物系统的特征，也是人工系统的特征，使自组织成为从物理学到生物学和工程学等几个学科的基础概念。处于学科前沿的人工生命（ALife）大量借鉴了自组织研究的概念和工具，为类似生命的现象提供了机械论的解释，也为人工系统设计提供了有用的建构主义方法。尽管自组织的概念在ALife中被广泛使用，但它经常被过度延伸或误解，要求澄清，以帮助追踪哪些可以和不能被认为是自组织的边界。在这篇综述中，我们讨论了自组织的基本方面，并列出了自组织在三个主要领域中的主要应用，即“软”（数学/计算建模）、“硬”（物理机器人）和“湿”（化学/生物系统）。我们还提供了一个分类来定位这项研究。最后，我们讨论了自组织和相关概念在ALife研究中的有用性，指出了未来研究的前景和挑战，并列出了有待解决的问题。我们希望这项工作将激发ALife和相关领域中与自组织相关的讨论。
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英文标题：
《Self-Organization and Artificial Life》
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作者：
Carlos Gershenson, Vito Trianni, Justin Werfel, Hiroki Sayama
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最新提交年份：
2020
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分类信息：

一级分类：Physics        物理学
二级分类：Adaptation and Self-Organizing Systems        自适应和自组织系统
分类描述：Adaptation, self-organizing systems, statistical physics, fluctuating systems, stochastic processes, interacting particle systems, machine learning
自适应，自组织系统，统计物理，波动系统，随机过程，相互作用粒子系统，机器学习
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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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一级分类：Computer Science        计算机科学
二级分类：Robotics        机器人学
分类描述：Roughly includes material in ACM Subject Class I.2.9.
大致包括ACM科目I.2.9类的材料。
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一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
  Self-organization can be broadly defined as the ability of a system to display ordered spatio-temporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several disciplines, from physics to biology and engineering. Placed at the frontiers between disciplines, Artificial Life (ALife) has heavily borrowed concepts and tools from the study of self-organization, providing mechanistic interpretations of life-like phenomena as well as useful constructivist approaches to artificial system design. Despite its broad usage within ALife, the concept of self-organization has been often excessively stretched or misinterpreted, calling for a clarification that could help with tracing the borders between what can and cannot be considered self-organization. In this review, we discuss the fundamental aspects of self-organization and list the main usages within three primary ALife domains, namely "soft" (mathematical/computational modeling), "hard" (physical robots), and "wet" (chemical/biological systems) ALife. We also provide a classification to locate this research. Finally, we discuss the usefulness of self-organization and related concepts within ALife studies, point to perspectives and challenges for future research, and list open questions. We hope that this work will motivate discussions related to self-organization in ALife and related fields.
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PDF链接：
https://arxiv.org/pdf/1903.07456

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关键词：自组织 Organization Quantitative Mathematical Intelligence 人工 计算 研究 systems concept