INTELLIGENT SYSTEMS: ARCHITECTURE, DESIGN, AND CONTROL, by Alexander M. Meystel and James R. Albus, Wiley-Interscience, New York, 2001, xxi + 696 pp., ISBN 0-471-19374-7 (Hardback, £55.95) Wiley Series on Intelligent Systems
INTELLIGENT SYSTEMS: ARCHITECTURE, DESIGN, AND CONTROL, by Alexander M. Meystel and James R. Albus, Wiley-Interscience, New York, 2001, xxi + 696 pp., ISBN 0-471-19374-7 (Hardback, £55.95) Wiley Series on Intelligent Systems
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《智能系统：架构、设计和控制》，Alexander M. Meystel 和 James R. Albus，Wiley-Interscience，纽约，2001 年，xxi + 696 页，ISBN 0-471-19374-7（精装本，55.95 英镑） Wiley 智能系统系列
DOI：
10.1017/s0263574701213952
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发表时间：
2002
期刊：
影响因子：
2.7
通讯作者：
A. Andrew
中科院分区：
文献类型：
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作者：
A. Andrew
这是一个全面的审查，并在某些方面重新思考，在自然和人工系统的智能的性质。定义的问题被处理，以及起源的想法，智能可以是一个功能的生活系统和人工制品一样。作者完全有资格阐述智能机器，因为他们多年来在开发工业机器人方面发挥了主导作用，更重要的是，自主陆地车辆是机器智能最令人信服的演示之一。这本书很可能是一门讲授课程的基础，在每一章的结尾都有测验和测试题。治疗有一个特定的倾斜，这当然必须承认，鉴于作者取得的实际成果是富有成效的。重点放在智能行为的“多分辨率”性质上，封面插图是第一位作者的一件名为“多分辨率思考者”的艺术品。这看起来类似于罗丹的“思想者”雕塑，但有一些较小的思考头部嵌套在主头部和彼此之间。这种处理方法允许感知，知识表示和规划的主题被单独考虑，从这个意义上说，它相当于“老式的人工智能”。承认最近的“寒武纪智能”的方法，由于罗德尼布鲁克斯，和其他简单的机器人的研究，其中感觉输入耦合，而直接到电机输出，但这些显然是有限的价值。对多分辨率的强调贯穿始终。可以看出，在感觉输入的分析中以简单的方式操作，其中例如来自视网膜的感觉元件的输入具有非常高的分辨率，但是在阶段中被削减到关于相对少量的重要对象和特征的描述。相反，行为的产生是分阶段进行的，从相对较少的目标到复杂得多的行动计划。这些过程中的连续阶段是分辨率的级别。有更多的理论比这个缩略图可能会建议，和过程的搜索，过滤和搜索，以及抽象，聚合和概括，介绍了作为组件。存储知识的结构被认为是必然的多分辨率，因为是智能本身。有一章是关于动机、目标和价值判断的，还有一章是关于学习的。与布鲁克斯所青睐的方法相反，组织和存储知识的需要使得这种方法本质上是符号化的，并且通过学习过程对符号进行适当的分配。这里提到了皮尔士对“符号”的讨论，特别是俄罗斯工作者德米特里·波斯佩洛夫（Dmitri Pospelov）对符号学理论的最新发展。讨论的一个项目是制造一个婴儿机器人，它将模拟早期的认知发展，并有可能与人类青少年进行比较。对这样一个项目的兴趣表明，其目标是对认知的深刻理解。符号学被定义为构建解释模型从而提取意义的艺术和科学。第653页介绍了符号学和数学之间的一种有趣的关系，数学被认为是不同的，因为它不需要在每一步都扎根于现实，但仍然充当着探索结构和关系的实验室，这些结构和关系随后可能适合于扎根并成为符号学的一部分。在最后一章的书，非常普遍的问题的性质，情报处理，与建议，标准的智能行为可能会发现，不依赖于参考生物学范式。系统来理解文本，例如总结和分类文件目前不存在，但它声称，正在取得进展，朝着“这个弗兰肯斯坦的世纪”，有许多发人深省的讨论的基本原则，其中上述观察数学和符号学之间的联系是一个例子。本章的标题是：“智能系统：科学与工程新范式的先驱”。发展的理论在较早的一部分，这本书有一个基本的实践基础，和多分辨率的例子被发现在这样的基本背景下的自动变化的步长范围内的数值积分，和类似的变化的粒度在有限元方法。许多插图显示了机器人的探索活动和其他实际结果等事情的记录。描述了工厂规划的应用，以及能够在杂乱的地形上选择道路，然后使用存储的信息更快地返回的自动驾驶车辆。另一个项目涉及一排自动驾驶汽车的协同操作。这本书写得很清楚，有许多有用的数字。与此同时，这是一本大部头的书，在每一个阶段都提出了大量不同的论点，这意味着要掌握它是一项艰巨的任务。这项工作肯定会被发现是非常值得的，无论是对先进机器人设备的开发人员和人工智能工作者，还是对心理学家和其他生物学家。
This is a comprehensive review, and in some ways a rethink, of the nature of intelligence in both natural and artificial systems. Questions of definition are treated, as well as the origins of the idea that intelligence can be a feature of living systems and artefacts alike. The authors are well qualified to expound on intelligent machines as they have played a leading part over many years in developing industrial robots and, even more significantly, autonomous land vehicles that are among the most convincing demonstrations yet made of machine intelligence. The book could very well be the basis of a taught course, and at the end of each chapter there are quizzes and test questions. The treatment has a particular slant, which certainly has to be acknowledged as productive in view of the practical results achieved by the authors. Emphasis is placed on the “multiresolutional” nature of intelligent behaviour, and the cover illustration is an artwork called “Multiresolutional Thinker” by the first-named author. This looks similar to the “Thinker” sculpture of Rodin but with a number of smaller thoughtful heads nested within the main one and within each other. The treatment allows the topics of perception, knowledge representation, and planning to be considered separately, and in this sense it amounts to “good old-fashioned AI ”. Acknowledgement is made of the recent “Cambrian intelligence” approach due to Rodney Brooks, and other studies of simple robots in which sensory input is coupled rather directly to motor output, but these are clearly felt to be of limited value. The emphasis on multiresolution is maintained throughout. It can be seen to operate in a simple way in the analysis of sensory input, where for example the input from the sensory elements of a retina is of very high resolution but is whittled down in stages to a description in terms of a relatively small number of significant objects and features. Conversely, behaviour generation proceeds in stages from a relatively small number of goals to a plan of action that is much more complex. The successive stages in these processes are levels of resolution. There is very much more to the theory than this thumbnail might suggest, and processes of Grouping, Filtering and Search, as well as Abstraction, Aggregation and Generalisation, are introduced as components. The structure of stored knowledge is argued to be necessarily multiresolutional, as is intelligence itself. There is a chapter on Motivations, Goals and Value Judgement, and one on Learning. The need to organise and store knowledge makes the approach essentially symbolic, in contrast to that favoured by Brooks, and an appropriate assignment of symbols is made by a learning process. There is reference here to the discussion of “signs” by Peirce, and particularly to a recent development of the theory of semiotics due to the Russian worker Dmitri Pospelov. A project that is discussed is the making of a baby-robot that would simulate early cognitive development, with the possibility of comparison with human youngsters. Interest in such a project demonstrates that the aim is very deep understanding of cognition. Semiotics is defined as the art and science of constructing models for interpretation and thus for meaning extraction. An intriguing relationship between semiotics and mathematics is introduced on page 653, with mathematics seen as differing in being free of the need to be grounded in reality at each step but nevertheless serving as a laboratory for exploration of structures and relationships that may subsequently be amenable to grounding and to becoming part of semiotics. In the final chapter of the book, very general issues of the nature of intelligence are treated, with the suggestion that criteria of intelligent behaviour might be found that do not depend on reference to the biological paradigm. Systems to understand text and for example to summarise and categorise documents do not currently exist but it is claimed that progress is being made towards “this Frankenstein of the twenty-first century”, and there is much thought-provoking discussion of underlying principles, of which the above observation on a connection between mathematics and semiotics is an example. The title of the chapter is: “Intelligent Systems: Precursor of the New Paradigm in Science and Engineering”. The development of the theory in the earlier part of the book has an essentially practical basis, and examples of multiresolution are found in such elementary contexts as the automatic variation of step size within a range of numerical integration, and similar variation of granularity in finite element methods. A number of the illustrations show records of such things as exploratory activity of robots, and other practical results. An application to factory planning is described, and also an autonomous vehicle able to pick its way across a cluttered piece of terrain and then to return much more quickly using stored information. Another project involves the collaborative operation of a platoon of autonomous vehicles. The book is clearly written, and there are numerous helpful figures. At the same time, it is a big book and the wealth of diverse arguments that are brought to bear at every stage means that getting to grips with it is a daunting task. The effort will certainly be found to be well worth while, both for developers of advanced robotic devices and workers in AI generally, and for psychologists and other biologists.