Author, Rolf Pfeifer, is interviewed on the Brain Science Podcast #25.

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Product Description

By the mid-1980s researchers from artificial intelligence, computer science, brain and cognitive science, and psychology realized that the idea of computers as intelligent machines was inappropriate. The brain does not run "programs"; it does something entirely different. But what? Evolutionary theory says that the brain has evolved not to do mathematical proofs but to control our behavior, to ensure our survival. Researchers now agree that intelligence always manifests itself in behavior-- thus it is behavior that we must understand. An exciting new field has grown around the study of behavior-based intelligence, also known as embodied cognitive science, "new AI," and "behavior-based AI."

This book provides a systematic introduction to this new way of thinking. After discussing concepts and approaches such as subsumption architecture, Braitenberg vehicles, evolutionary robotics, artificial life, self-organization, and learning, the authors derive a set of principles and a coherent framework for the study of naturally and artificially intelligent systems, or autonomous agents. This framework is based on a synthetic methodology whose goal is understanding by designing and building.

The book includes all the background material required to understand the principles underlying intelligence, as well as enough detailed information on intelligent robotics and simulated agents so readers can begin experiments and projects on their own. The reader is guided through a series of case studies that illustrate the design principles of embodied cognitive science.

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Product Details

• Amazon Sales Rank: #533058 in Books
• Published on: 2001-09-01
• Original language: English
• Number of items: 1
• Binding: Paperback
• 720 pages

Amazon.com Review
Most artificial intelligence seems artificially dumb. Sure, Deep Blue can beat a chess grand master two games out of three, but could it get out of the way of an oncoming bus? AI researchers are coming to understand that if we want more than idiot savants for machines, we'll need to build them from the ground up--a behavior-based approach. Rolf Pfeifer, head of the Artificial Intelligence Laboratory at the University of Zurich, and Christian Scheier, postdoctoral fellow at the University of Zurich and Caltech, have put together Understanding Intelligence, the definitive introduction to this approach for students, amateurs, and professionals alike. As they admit, there are plenty of gaps in our knowledge, so they take pains to make our ignorance as well as our knowledge explicit, and encourage thinking beyond the text with "Issues to Think About" at the end of each chapter.

Delving into neural networks, subsumption architecture, principles and design of intelligent systems, and future applications, the authors strive to exhaust the literature and compress it into concise, readable text with plenty of illustrations where appropriate. Given the freshness of the material, it feels less like a textbook and more like a treasure map--we don't know what we'll find when we get there, but we know it's going to be good. Whether robotics is a career, a hobby, or a side interest for you, Understanding Intelligence will help you get to work from the bottom up. --Rob Lightner

Review
"Understanding Intelligence is comprehensive and highly readable introduction to embodied cognitive science. It will be particularly helpful for people interested in getting involved in the construction of intelligent agents."
— Arthur B. Markman, Science

"People trained in classical AI will find this book an articulate and thought-provoking challenge to much that they have taken for granted. People new to cognitive science will find it a stimulating introduction to one of the field's most productive controversies. Pfeifer and Scheier deserve our thanks for a thorough, accessible, and courteous contribution in the best tradition of scholarly debate."
— H. Van Dyke, Computing Reviews

About the Author
Rolf Pfeifer is Professor of Computer Science and Director of the Artificial Intelligence Laboratory in the Department of Informatics at the University of Zurich. He is the author of Understanding Intelligence (MIT Press, 1999).

Christian Scheier is a Postdoctoral Fellow at the California Institute of Technology, Pasadena, California.

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Customer Reviews

Great overview
I liked this book because it not only gives a conceptual tour d'horizon on the new field of embodied cognitive science/New AI, but also provides tons of concrete (programming) problems to work with. The companion web site contains additional programming examples for download. Overall, this book gave me the much needed realistic perspective on this new field.

Excellent intro to embodied AI
I'm a masters student in general CS doing a semester of school at the University of Sussex in England. One of the courses that I took used Understanding Intelligence as a reccomended text. The book covers in detail a lot of aspects of embodied artificial intelligence and of some of the conflicts between embodied AI and traditional symbolic AI. The book is easy to read and, at least for me, quite worth it. The only problem that I found was that the books website is obscurely documented in the book and is quite low budget. Other than that it's a great introduction to the field; I found it much easier to assimilate than the lectures from my class.

A return to behaviorism?
The new book UNDERSTANDING INTELLIGENCE by Pfeifer and Scheier contains many interesting ideas and is well worth reading though rather long (at nearly 700 pages). In many ways it is similar to the claim that "intelligence is a kludge" but this time more of a hardware kludge. There is also a substantial return to behaviorism though the references are missing. Perhaps the core belief of the book is that "intelligence is...a large number of parallel loosely coupled processes" (page 303). But it seems to me that if the modules are too loosely coupled the system can not be successful as a universal approximator and therefore will not be Turing equivalent. It might be possible to decompose the performance system of an agent into such loosely coupled modules (like Norvig and Russell, AI a Modern Approach, page 202) but for it to learn there must be extensive coupling to most if not all modules. While I agree that sensory motor contact with the world is what grounds symbols there is no need for every agent to perform its own grounding of every symbol. In fact there are modules (agents) in a typical subsumption architecture that are not, themselves, connected to either sensors or actuators. I do like the idea that intelligence is related to some niche. Humans and AIs may not occupy the same niche. Machines are better at statistics than humans while humans are better at emotion. The criticism of GOFAI in UNDERSTANDING INTELLIGENCE is often unfounded.