稳健多细胞展开设计

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摘要翻译：
本文介绍了一种用于多细胞发育设计的连续模型。细胞被固定在二维网格上，在生长过程中与邻居交换“化学物质”。一个细胞产生的化学物质的数量，以及细胞在表型中的分化值，由一个神经网络（基因型）控制，该神经网络以相邻细胞在前一个时间步长产生的化学物质为输入。在所提出的模型中，生长过程的迭代次数不是预先确定的，而是在进化过程中出现的：只有生长过程稳定的生物才给出一种表型（稳定状态），其他的生物被宣布为不可行的。控制器的优化采用NEAT算法，该算法对神经网络的拓扑结构和权值进行了优化。尽管每个细胞只从其邻居处接收局部信息，但在“flags”问题（表型必须匹配给定的二维模式）上，所提出的方法的实验结果几乎与使用具有全局信息的相同模型的直接回归方法的结果一样好。而且，由此产生的多细胞有机体表现出近乎完美的自愈特性。
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英文标题：
《Robust Multi-Cellular Developmental Design》
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作者：
Alexandre Devert (INRIA Futurs), Nicolas Bred\`eche (INRIA Futurs),
  Marc Schoenauer (INRIA Futurs)
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最新提交年份：
2007
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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：
  This paper introduces a continuous model for Multi-cellular Developmental Design. The cells are fixed on a 2D grid and exchange "chemicals" with their neighbors during the growth process. The quantity of chemicals that a cell produces, as well as the differentiation value of the cell in the phenotype, are controlled by a Neural Network (the genotype) that takes as inputs the chemicals produced by the neighboring cells at the previous time step. In the proposed model, the number of iterations of the growth process is not pre-determined, but emerges during evolution: only organisms for which the growth process stabilizes give a phenotype (the stable state), others are declared nonviable. The optimization of the controller is done using the NEAT algorithm, that optimizes both the topology and the weights of the Neural Networks. Though each cell only receives local information from its neighbors, the experimental results of the proposed approach on the 'flags' problems (the phenotype must match a given 2D pattern) are almost as good as those of a direct regression approach using the same model with global information. Moreover, the resulting multi-cellular organisms exhibit almost perfect self-healing characteristics.
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PDF链接：
https://arxiv.org/pdf/0705.1309

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