X-Received: by 2002:ae9:c002:0:b0:742:72ce:2710 with SMTP id u2-20020ae9c002000000b0074272ce2710mr1138401qkk.2.1677355934522; Sat, 25 Feb 2023 12:12:14 -0800 (PST) X-Received: by 2002:a05:622a:4204:b0:3bd:1820:b569 with SMTP id cp4-20020a05622a420400b003bd1820b569mr3925611qtb.9.1677355934288; Sat, 25 Feb 2023 12:12:14 -0800 (PST) Path: csiph.com!weretis.net!feeder8.news.weretis.net!proxad.net!feeder1-2.proxad.net!209.85.160.216.MISMATCH!news-out.google.com!nntp.google.com!postnews.google.com!google-groups.googlegroups.com!not-for-mail Newsgroups: comp.ai.edu Date: Sat, 25 Feb 2023 12:12:14 -0800 (PST) In-Reply-To: <1994Nov30.151429.14332@cs.cornell.edu> Injection-Info: google-groups.googlegroups.com; posting-host=171.33.255.215; posting-account=oklevQoAAADWyhgilQw4z6rEa3NM1Xnc NNTP-Posting-Host: 171.33.255.215 References: <1994Nov30.151429.14332@cs.cornell.edu> User-Agent: G2/1.0 MIME-Version: 1.0 Message-ID: Subject: Re: A review of Russell and Norvig's new AI text From: =?UTF-8?B?0JXQutCw0YLQtdGA0LjQvdCwINCi0LjQvNC+0YTQtdC10LLQsA==?= Injection-Date: Sat, 25 Feb 2023 20:12:14 +0000 Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: quoted-printable Xref: csiph.com comp.ai.edu:417 On Wednesday, November 30, 1994 at 10:14:29=E2=80=AFPM UTC+7, Devika Subram= anian 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 >=20 > 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. >=20 > 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.