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摘要翻译：
符号控制器的思想试图弥合Brooks的包容体系结构所倡导的自上而下的控制器体系结构手工设计和自下而上的无设计师方法之间的鸿沟，后者现在是进化机器人社区的标准。设计者提供了一组基本行为，进化被赋予了将它们集合起来以解决复杂任务的目标。文中给出了两个实验，证明了该方法的有效性和递归性。特别是，对所提出的基本行为的敏感性，和鲁棒性W.R.T。对所得控制器的推广进行了详细的研究。
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英文标题：
《Evolving Symbolic Controllers》
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作者：
Nicolas Godzik (INRIA Futurs, INRIA Rocquencourt), Marc Schoenauer
(INRIA Futurs, INRIA Rocquencourt), Mich\`ele Sebag (INRIA Futurs, LRI)
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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：
The idea of symbolic controllers tries to bridge the gap between the top-down manual design of the controller architecture, as advocated in Brooks' subsumption architecture, and the bottom-up designer-free approach that is now standard within the Evolutionary Robotics community. The designer provides a set of elementary behavior, and evolution is given the goal of assembling them to solve complex tasks. Two experiments are presented, demonstrating the efficiency and showing the recursiveness of this approach. In particular, the sensitivity with respect to the proposed elementary behaviors, and the robustness w.r.t. generalization of the resulting controllers are studied in detail.
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PDF链接：
https://arxiv.org/pdf/0705.1244
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