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Re: A review of Russell and Norvig's new AI text

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On Wednesday, November 30, 1994 at 10:14:29 PM UTC+7, Devika Subramanian wrote:
> A brief review of
> "Artificial Intelligence: A Modern Approach"
> Stuart Russell and Peter Norvig
> Prentice Hall, December 1994. ISBN 0-13-103805-2
> by Devika Subramanian, Cornell University
> 
> While the enterprise of artificial intelligence has often been defined
> around the dream of intelligent agents, Russell and Norvig's book is
> the first attempt to present the technical accomplishments of AI to a
> broad scientific audience in the context of embedded agents acting in
> real-world environments. The book is not merely an expositional
> triumph; Russell and Norvig achieve a unique synthesis of concepts and
> algorithms in AI that have evolved in very disparate sub-communities
> of the field. The book draws on ideas from logic, decision theory,
> control theory, Markov processes, economics, on-line algorithms,
> complexity theory, probability and statistics and information theory,
> to coherently present methods in AI in a jargon-free manner. This
> makes the book an ideal introduction to newcomers to AI from computer
> science as well as other branches of science and engineering. For
> seasoned practitioners, it offers a new, thought-provoking way to
> understand AI.
> The book is organized into eight sections. The first section begins
> with a brief history of AI and introduces the basic vocabulary for
> describing agents embedded in task environments. The last section
> (Section VIII) comprises a beautiful essay on the philosophical
> foundations of AI and an engaging commentary on the current state and
> future challenges facing AI. The sections in between constitute the
> technical meat of the book. Section II highlights general
> problem-solving methods for embedded agents and includes informed
> search methods that take resource constraints into account. The third
> section emphasizes the role of knowledge in decision-making and
> presents an array of methods for representing and reasoning with
> logical or categorical knowledge. Section IV presents planning as
> reasoning about action choice; contemporary planning and replanning
> methods are presented as specializations of the general methods of
> logical reasoning introduced in the third section. Section V
> introduces probability and decision theory as tools for agents acting
> under uncertainty. It explains how belief networks can be used to
> represent uncertain knowledge and describes decision-making methods
> based on them. The sixth section focuses on learning and adaptation in
> intelligent agents. It presents a unified model of learning, a brief
> introduction to computational learning theory, as well as specific
> techniques such as decision-tree learning, neural networks, and a new
> method for learning belief networks. It also includes a tutorial
> exposition of recent work in reinforcement learning, as well as the
> knowledge-based inductive logic programming method. Section VII
> focuses on interactions of the agent with the external world: natural
> language communication, perception and robotics. Russell and Norvig
> have recruited established experts (Jitendra Malik and John Canny) to
> cover the specialized topics of perception and robotics, ensuring a
> uniformly high quality to all of the technical material in the book.
> The book is hefty: over 900 pages in all. However, almost 200 pages
> are devoted to items sometimes missing from AI texts: a very thorough
> index, a truly massive bibliography, "Historical Notes" sections that
> are researched in depth and make fascinating reading, and a large
> collection of excellent exercises.
> This is perhaps not the place to go through all the book's chapters in
> detail, but some deserve special mention. The second chapter on
> agents is brilliant; it puts the entire history of work in AI in
> perspective and explains WHY people built the algorithms that were
> built. This is the first question that most first-timers to AI have,
> and this is answered up front. The chapters on reasoning about
> uncertainty are by far the best tutorial exposition of material on
> probability and belief networks: they make the original papers in the
> area much more accessible.
> Judged from all respects, this is a remarkably comprehensive and
> incisive treatment of the field. The book is well-written and
> well-organized and includes uniform and clear descriptions of all
> major AI algorithms. The authors have managed to describe key
> concepts with technical depth and rigour without falling prey to
> stodginess and Greek-symbolitis. AI is presented as a set of
> inter-related design principles, rather than a grab bag of tricks.
> The book brims with optimism and contagious excitement about the
> frontiers of AI. I recommend it without reservation to anyone
> interested in the computational study of intelligence, whether they be
> undergraduate or graduate students or senior scientists in the field.
> 
> About the reviewer: Subramanian is an Assistant Professor at the
> Computer Science Department at Cornell University. Her interests are
> in AI, its theoretical foundations and practical applications in
> design, scheduling and molecular biology. She has been teaching AI at
> the undergraduate and graduate levels for about five years.

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Re: A review of Russell and Norvig's new AI text Екатерина Тимофеева <victoria.sp.76@gmail.com> - 2023-02-25 12:12 -0800

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