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摘要翻译：
机器人控制器软件的半自动或自动综合既是一个理想的问题，也是一个具有挑战性的问题。通过应用人工进化来合成相当简单的行为，如避免碰撞，已经被多次展示。然而，随着机器人所要完成的任务越来越复杂，这种综合的难度也越来越大。我们试图用人工稳态激素系统(AHHS)来解决这个复杂的问题，它既提供固有的稳态过程，又提供（瞬时的）固有的变异行为。通过使用AHHS，对预定义控制器拓扑结构或关于应用领域的信息的需求被最小化。我们研究了控制器的原理设计和激素网络的大小如何影响人工进化的整体性能（即进化性）。这是通过比较两种变异的AHHS来实现的，这两种变异的AHHS在变异时表现出不同的效果。我们为一个机器人进化出一个控制器，它由五个自主的、协作的模块组成。所需的行为是通过使用模块的主要铰链导致快速运动的步态形式。
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英文标题：
《Artificial Hormone Reaction Networks: Towards Higher Evolvability in
Evolutionary Multi-Modular Robotics》
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作者：
Heiko Hamann, J\"urgen Stradner, Thomas Schmickl, Karl Crailsheim
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最新提交年份：
2010
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分类信息：
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：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：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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英文摘要：
The semi-automatic or automatic synthesis of robot controller software is both desirable and challenging. Synthesis of rather simple behaviors such as collision avoidance by applying artificial evolution has been shown multiple times. However, the difficulty of this synthesis increases heavily with increasing complexity of the task that should be performed by the robot. We try to tackle this problem of complexity with Artificial Homeostatic Hormone Systems (AHHS), which provide both intrinsic, homeostatic processes and (transient) intrinsic, variant behavior. By using AHHS the need for pre-defined controller topologies or information about the field of application is minimized. We investigate how the principle design of the controller and the hormone network size affects the overall performance of the artificial evolution (i.e., evolvability). This is done by comparing two variants of AHHS that show different effects when mutated. We evolve a controller for a robot built from five autonomous, cooperating modules. The desired behavior is a form of gait resulting in fast locomotion by using the modules' main hinges.
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PDF链接：
https://arxiv.org/pdf/1011.3912
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