摘要翻译：
提出了一种消除布尔自动机转换表中冗余的方法：双符号模式重述。一个符号用于捕获单个输入变量的冗余，另一个符号用于捕获输入变量集合中的可置换性：充分表征布尔函数中存在的渠化。双符号图式解释了自动机网络行为的一些方面，它们的涌现模式的特征无法捕捉到这些方面。我们用我们的方法比较了两个著名的用于密度分类任务的细胞自动机：人类工程CA GKL和另一个通过遗传编程(GP)获得的细胞自动机。我们表明，尽管有非常不同的集体行为，这些规则是非常相似的。的确，GKL是GP的一个特例。因此，我们证明了通过图式重新描述细胞自动机的规则比通过观察它们的突现行为来比较细胞自动机更可行，这使我们质疑复杂性研究中更多地关注突现模式而不是局部相互作用的趋势。
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英文标题：
《Schema Redescription in Cellular Automata: Revisiting Emergence in
  Complex Systems》
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作者：
Manuel Marques-Pita and Luis M. Rocha
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最新提交年份：
2011
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分类信息：

一级分类：Physics        物理学
二级分类：Cellular Automata and Lattice Gases        元胞自动机与格子气体
分类描述：Computational methods, time series analysis, signal processing, wavelets, lattice gases
计算方法，时间序列分析，信号处理，小波，格子气体
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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：Formal Languages and Automata Theory        形式语言与自动机理论
分类描述：Covers automata theory, formal language theory, grammars, and combinatorics on words. This roughly corresponds to ACM Subject Classes F.1.1, and F.4.3. Papers dealing with computational complexity should go to cs.CC; papers dealing with logic should go to cs.LO.
涵盖自动机理论，形式语言理论，文法，和词的组合学。这大致相当于ACM主题类F.1.1和F.4.3。处理计算复杂性的论文应该上CS.CC；处理逻辑的论文应该去CS.LO。
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一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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一级分类：Quantitative Biology        数量生物学
二级分类：Quantitative Methods        定量方法
分类描述：All experimental, numerical, statistical and mathematical contributions of value to biology
对生物学价值的所有实验、数值、统计和数学贡献
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英文摘要：
  We present a method to eliminate redundancy in the transition tables of Boolean automata: schema redescription with two symbols. One symbol is used to capture redundancy of individual input variables, and another to capture permutability in sets of input variables: fully characterizing the canalization present in Boolean functions. Two-symbol schemata explain aspects of the behaviour of automata networks that the characterization of their emergent patterns does not capture. We use our method to compare two well-known cellular automata for the density classification task: the human engineered CA GKL, and another obtained via genetic programming (GP). We show that despite having very different collective behaviour, these rules are very similar. Indeed, GKL is a special case of GP. Therefore, we demonstrate that it is more feasible to compare cellular automata via schema redescriptions of their rules, than by looking at their emergent behaviour, leading us to question the tendency in complexity research to pay much more attention to emergent patterns than to local interactions.
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PDF链接：
https://arxiv.org/pdf/1102.1691