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Paradigms of Artificial Intelligence: A Methodological and Computational Analysis [Paperback]

By Achim G. Hoffmann (Author)
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Item description for Paradigms of Artificial Intelligence: A Methodological and Computational Analysis by Achim G. Hoffmann...

This book presents a new methodological analysis of the two competing research paradigms of artificial intelligence and cognitive science: the symbolic versus the connectionist paradigm. It argues that much of the discussion put forward for either paradigm misses the point: Most of the arguments in the debates on the two paradigms concentrate on the question whether the nature of intelligence or cognition is properly accommodated by one or the other paradigm. Opposed to that is the analysis in this book, which concentrates on the question which of the paradigms accommodates the "user" of a developed theory or technique. The user may be an engineer or a scientist - in any case, the "user" has to be able to grasp the theory and to competently use the methods which are developed. Consequently, besides the nature of intelligence and cognition, the "user" must be the focus of the debate on the proper paradigm. From the relationship between the mental capacities of the "user" and certain aspects of the nature of intelligence and cognition, the book derives new objectives for future research which will help to integrate aspects of both paradigms to obtain more powerful AI techniques and to promote a deeper understanding of cognition. . The book presents the fundamental ideas of both, the symbolic as well as the connectionist paradigm. Along with an introduction to the philosophical foundations, an exposition of some of the typical techniques of each paradigm is presented in the first two parts. This is followed by the mentioned analysis of the two paradigms in the third part. The book is intended for researchers, practitioners, advanced students, and interested observers of the developing fields of artificial intelligence and cognitive science. Providing accessible introductions to the basic ideas of both paradigms, it is also suitable as a textbook for a subject on the topic at an advanced level in computer science, philosophy, cognitive science, or psychology.

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Item Specifications...

Studio: Springer
Pages   332
Est. Packaging Dimensions:   Length: 9.16" Width: 6.1" Height: 0.63"
Weight:   1.28 lbs.
Binding  Softcover
Release Date   Dec 21, 1998
Publisher   Springer
ISBN  9813083972  
ISBN13  9789813083974  

Availability  97 units.
Availability accurate as of May 29, 2017 03:02.
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Product Categories

1Books > Special Features > New & Used Textbooks
2Books > Subjects > Computers & Internet > Computer Science > Artificial Intelligence > Computer Mathematics
3Books > Subjects > Computers & Internet > Computer Science > Artificial Intelligence > General
4Books > Subjects > Computers & Internet > Computer Science > Artificial Intelligence > Theory of Computing
5Books > Subjects > Computers & Internet > General
6Books > Subjects > Nonfiction > Philosophy > Epistemology
7Books > Subjects > Science > General
8Books > Subjects > Science

Reviews - What do customers think about Paradigms of Artificial Intelligence: A Methodological and Computational Analysis?

A very thought provoking book, but could be improved.  Aug 3, 2001
Paradigms of AI is comprised of three parts: symbolic-paradigm overview, connectionist-paradigm overview, and a critique of both paradigms. The first two parts make up over half the book, and after reading them I was left wondering why the author bothered to spend that many pages on them. Someone new to AI will not gain much from the terse overviews, and someone well versed in AI will probably want to skip these first two parts. On the other hand, the connectionist overview had a few interesting sections on subjects that were new to me: adaptive resonance theory, self-organizing maps, and the integration of symbols into connectionist models. That being said, I felt the third part should have been expanded (i.e. made up more of the book) in a way that gave more concrete examples of the sometimes vague concepts the author attempted to relate. For example, why not place the mathematical treatment related to the computational limitations of connectionist networks from the appendix to an earlier chapter, where it can be well-developed and read so as to support the hand waving? But even more annoying may have been the author's lack of consideration of the computational complexity of connectionist networks, and his failure to mention how unlikely it is that a Turing machine could succeed in simulating a human brain (yes time matters!). He mentions this in a footnote, but does not spend time on the matter. May be the most interesting part of the book was his exploration of the meaning of symbols, and presenting the connectionist/symbolic debate from a phenomenological point of view. The main message seemed to be that AI systems need to take the scientist or engineer into consideration. In other words, what good is a paradigm if it cannot be understood and controlled? Thus, there must be third-person symbols for helping engineers to manage the complexity of the computing paradigm. Although I agree with this from a "practical" point of view, from an investigative point of view focusing only on systems whose computations and modus operandi can be understood seems to be missing an important perspective of AI; that being that the most intelligent systems (humans) living on our planet are sometimes unpredictable, yet are very useful. I believe there will be a time when humans work side-by-side with artificial machines that are not easily predictable or understood, but useful none-the-less. In closing, I recommend part three of this book to anyone who has an interest in the philosophy of arificial intelligence. The first two parts can be safely be omitted.
A detailed review  Sep 14, 2000
This book takes a look at the paradigms of two different approaches to artificial intelligence; the symbolic paradigm and the connectionist paradigm. The symbolic approach uses a mathematically oriented way of abstractly describing processes leading to intelligent behaviour, while the rather physiologically oriented connectionist approach, favoured the modelling of brain functions in order to reverse-engineer intelligence. The symbolic paradigm has always been the dominant one following the highly influential analysis of the capabilities and limitations of the perceptron. However the brain-oriented connectionist paradigm emerged to challenge the traditional symbolic paradigm, which was said to be unsuccessful since symbols are insufficient to model crucial aspects of cognition and intelligence.

This book is quite unique in that it takes a step back from the heated debate between the advocates of both paradigms, and gives a neutral standpoint encouraging the reader to make a decision himself. It delivers a methodological analysis of the virtues and vices of both the symbolic and the connectionist paradigm, which most books have often neither appreciated nor really addressed. But the book is much more than a mere checklist of the differences between the two paradigms. The book sets out to develop criteria, which any successful method for building AI systems and any successful theory for understanding cognition has to fulfil. The major theme of this book are methodological considerations regarding the form and purpose of a theory, which could and should be the outcome of scientific endeavours in AI and cognitive science.

Furthermore the author addresses the human subject who is to perform the design or who wants to understand a theory of cognition. The specific capabilities and limitations of the human subject to understand a theory or a number of design steps needs to be an instrumental criterion in deciding which of the paradigms is more appropriate. Furthermore, the human subject's capabilities and limitations have to provide the guideline for the development of more suitable frameworks for AI and cognitive science.

I have only given praise to the author and his book, and why shouldn't I? I am his student, and so know what a great teacher he is. Every Thursday afternoon I attend his Machine Learning lecture and in the same evening I attend his Neural Networks lecture at a world-renowned university. He is a very kind and gentle person, and his lectures are never lacking in humour. While always encouraging the students to think and actively participate, his lectures are well planned and thought provoking, very much like the book in question. I hope this book will give you the opportunity to have a taste of what Dr Achim's lectures are like.


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