The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications. KEY TOPICS: Intelligent Agents. Solving Problems by Searching. Informed Search Methods. Game Playing. Agents that Reason Logically. First-order Logic. Building a Knowledge Base. Inference in First-Order Logic. Logical Reasoning Systems. Practical Planning. Planning and Acting. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural Networks. Reinforcement Learning. Knowledge in Learning. Agents that Communicate. Practical Communication in English. Perception. Robotics. MARKET: For computer professionals, linguists, and cognitive scientists interested in artificial intelligence.
This monumental work, which completely dominates the AI textbook market, has been compared with classics like Watson's Molecular Biology of the Cell, and eminently succeeds in its goal of providing a clear, single-volume summary of the whole field of Artificial Intelligence. As pointed out on the book's home page, it is used in over 1200 universities in over 100 countries, and is the 25th most cited publication on Citeseer and the 2nd most cited publication of this century. The occasional suggestion you may hear that it "has passed its sell-by" or "gives a decent picture of Good Old-Fashioned AI" can unhesitatingly be written off as envious carping from academics who wish they'd got something even a tenth as impressive on their CVs.
What was that? Ah, yes, as a matter of fact it does cite one of my papers. How did you guess?
Heh, I opened this up to find the ISBN and found dried blood all over the pages, suggesting I read this during my cocaine-intensive period back in 1999-2000. That's fitting, since cocaine and the study of artificial intelligence seem to enjoy several similarities -- incredible expense as a barrier to entry, exciting short-term effects (see: euphoria, A* search) but letdowns upon prolonged use (see: addiction, combinatorial explosions), and they've both ruined plenty of fine careers in computer science. We used this book for CS4600, but I only got halfway though that semester and remember little of it (see: careers in computer science, aforementioned negative effects of cocaine on). I went back and read most of this in 2003, and found solid coverage of most everything useful I'm aware of from AI.
Wants a book that explains broad and deep AI yet in laymen term (nearly)? This is IT. Of all the AI books I have read, this one is arguably the most accessible to undergrads (CS, EE background) It assumes only minimal mathematical formalities and pretty much the maths things are self-contained. The authors did a great job of keeping the contents up-to-date with the latest happenings in AI, while keeping the readers sane. Overall, thumbs up!
Holy balls this book has a lot of pages. I also don't know why these things always have to have separate ``international'' editions.
It starts off strongly for a few hundred pages, but then for no reason at all devotes several chapters to high school-level probability and statistics, before devolving into essentially pointless mathematical show-boating for another few hundred pages. Then it finishes off with an interesting but not really relevant and highly unrigorous (not to mention typo-ridden) overview of Google's various products (mostly PageRank and Google Translate). There's a few more chapters after that, but I think it's best to pretend they don't exist. Chapter 26 (Philosophical Foundations), in particular, was a fucking embarrassment, giving more unnecessary to idiots like John Searle and Ray Kurzweil, and wasting paper on absurd hand-wringing over off-the-wall science-fiction scenarios. AI is too legitimate and interesting a field to justify that sort of crap in a university textbook.
In spite of all that, though, it's still a very good book, and a good overview of the field. I particularly liked that each chapter had an extensive section with historical and biographical notes at the end. If nothing else, it at least demonstrates that if the AI winter was ever a real thing (at least in terms of research activity and progress), it's far behind us now.
5 stars because there is, quite simply, no substitute.
Artificial Intelligence is, in the context of the infant science of computing, a very old and very broad subdiscipline, the "Turing test" having arisen, not only at the same time, but from the same person as many of the foundations of computing itself. Those of us students of a certain age will recall terms like "symbolic" vs. "connectionist" vs. "probabilistic," as well as "scruffies" and "neats." Key figures, events, and schools of thought span multiple institutions on multiple continents. In short, a major challenge facing anyone wishing to survey Artificial Intelligence is simply coming up with a unifying theme.
The major accomplishment, in my opinion, of AIMA, then, is that: Russell and Norvig take the hodge-podge of AI research, manage to fit it sensibly into a narrative structure centered on the notion of different kinds of "agents" (not to be confused with that portion of AI research that explicitly refers to its constructs as "agents!") and, having dug the pond and filled it with water, skip a stone across the surface. It's up to the reader whether to follow the arcs of the stone from major subject to major subject, foregoing depth, or whether to pick a particular contact point and concentrate on the eddies propagating from it. For the latter purpose, the extensive bibliography is indispensable.
With all of this said, I have to acknowledge that Russell and Norvig are not entirely impartial AI practitioners. Norvig, in particular, is well-known by now as a staunch Bayesian probabilist who, as Director of Search Quality or Machine Learning or whatever Google has decided to call it today, has made Google the Bayesian powerhouse that it is. (Less known is Norvig's previous stint at high-tech startup Junglee, which was acquired by Amazon. So to some extent Peter Norvig powers both Google and Amazon.) So one can probably claim, not without justification, that AIMA emphasizes Bayesian probability over other approaches.
Finally, as good as AIMA is, it is still a survey. Even with respect to Bayesian probability, the treatment is introductory, as I discovered with some shock upon reading Probability Theory: The Logic of Science. That's OK, though: it's the best introduction I've ever seen.
So read it once for the survey, keep it on your shelf for the bibliography, and refer back to it whenever you find yourself thinking "hey, didn't I read about that somewhere before?"
The Bible on computational decision-making. I use this term as this book is not just about the AI/machine learning we consistently hear about, it’s much more. This textbook tends to perfection, with no stone left unturned. Looking forward to the next edition, which, at the accelerating rate of innovation, looks overdue (the following sentence surely feels outdated: “Current Go programs play at the master level on a reduced 9 × 9 board, but are still at advanced amateur level on a full board”). There are 2 aspects I particularly enjoyed, (1) the historical sections at the end of each chapter; the introduction also gave a fascinating history of AI and its relationship to other fields (neurology, logics, cybernetics…). (2) I also liked all the gaming aspects, such as the Wumpus World which I didn't know before. I truly wish I had discovered that book when it was first published in 1995, sigh
يعتبر هذا الكتاب أهم مرجع للدارس في مجال الذكاء الاصطناعي. الكتاب يعتبر مقدمة لمواضيع كبيرة جدا و متشعبة، فعيتبر بداية التخصص في الذكاء الاصطناعي. يتناول الكتاب مواضيع في تعلم الآلة وخوارزميات البحث وحل المشكلات. بعض أجزاء الكتاب تعبر من المراجع النادرة وخصوصا في فيما يتعلق بموضع الـ reinforcement learning. مؤلفي الكتاب بيتر نورفق و روسيل من الرواد في مجال الذكاء الاصطناعي.
For a textbook, this is amazingly accessible and interesting. if you have any interest in the topic, this is the book to read. It's $100 or more, but it's very popular for AI classes, so any good college library should have a copy.
A fantastic textbook that's not only a great introduction to AI but also serves as a survey course in technical writing. I only read about 75% of it but definitely plan on revisiting it. Re-reading some earlier chapters taught me how much I missed on a first read (or forgot).
AIMA doesn't presume a ton of background beyond some programming experience, exposure to mathematical notation, and a basic understanding of computational complexity/algorithmic efficiency.
The first 10 chapters or so are the best and the second half of the book can be a bit of a trudge as it devolves into mathematical masturbation. A lot of the chapters are better served by other resources – I highly recommend the CS188 lectures from UC Berkeley for supplementation. Unfortunately, some chapters are straight up bad (the chapter on Philosophical Foundations comes to mind), but these tend to be few and far between.
Despite that, there is no more comprehensive book on AI. Read this, re-read this, and treat it with care – you will reap the rewards for a long time to come.
I have read only two chapters out of total seven (which was the plan) and the 3rd edition is already out of date regarding most areas of research (especially deep learning). It is a great book nonetheless.
OK so I did not read this cover to cover, but I did look closely at much of what you might call the foundational chapters, just to see 1. is there such a thing as AI, or are we just hoping there will be and 2. what can I learn as a philosopher from AI, whether it exists or not. Goal 2 was much more important as I teach a logic of induction class and of course one major pillar of AI would be developing machines that can perform judgments under uncertainty and apply rational heuristics as well as humans do (which is not very well at all by the way). I found out that I already knew most of this, from studies of Bayesian reasoning (which is very tricky by the way and should not be blindly implemented like this without a clear view of the limitations), and the study of acyclic causal graphs (which is standard academy reading for philosophers). These graphs also admit of howlers and counterexamples as anyone knows. I am more interested in the idea of developing "stupid machines" that function more like neural networks and less like probability maximizers. The human brain is fundamentally (in my view anyway) a stupid-machine, full of crazy workarounds and faulty logic. The correct solution or path is virtually never the one evolution comes up with, it just grinds it out with massive armies of neurons and interconnections and lots of trial and error, but nothing one would call a computation, as in Turing machines. Elegant algorithms for computer vision have, I believe, nothing to do with the way the brain constructs the visual image. One philosopher's take.
A comprehensive course in modern AI topics. While the book is dense with information, the authors provide clear explanations that will be easily picked up by the careful reader. An excellent companion to an undergraduate course in artificial intelligence.
It was written more like a text book for undergrads with extensive coverage of many topics. However, I was looking for more in-depth information on knowledge representation. But, it was too superficial for my need. May be, in 3rd edition it encompassed the latest ideas in this area.
کتاب کمی گنگ و قدیمی هست و معمولا کتب جدید هوش مصنوعی هم درک بهتری به موضوع میدن و هم میدان معرفی بزرگتر و سادگی بالاتری را شامل می شوند. در این کتاب بیشتر به حل مسائلی چون مسیریابی و الگوریتم های اینچنینی پرداخته شده
Considering my previous knowledge of Artificial Intelligence, this book was shite. Constant repetition of previously learnt algorithms and those hideous chapters on logic made me want to puke. The humor was good and the writer came off as friendly, but Jesus was this a waste of time.
Read the some of the parts relevant to my AI course. The standard book in the field of traditional AI techniques, great historical information/case studies in each chapter, but a lot of modern research in AI (deep learning) is based on very different principles, which are mentioned in, but not the focus of this book. Still would call it mandatory reading for anyone who wants to work in AI.
This is THE book to read on anything to do with modern artificial intelligence. I regard this as my personal bible and would recommend it to anyone who is involved in technical artificial intelligence.
TL;DR: Um excelente livro para quem quer estudar fundamentos de IA, recomendo.
O livro é bastante teórico. Li de forma despretensiosa, sem me preocupar com os exercícios, por exemplo. Meu objetivo, desde o início, foi ganhar uma base para continuar os estudos com o curso de Machine Learning de Stanford no Coursera  ou com um livro prático de IA aplicada .
Várias definições são detalhadamente explicadas no livro, diversos conceitos como:
- conhecimento (representação do conhecimento, incerteza) - inteligência e aprendizado (como forma de melhorar o desempenho) * aprendizagem indutiva (supervisionada ou não supervisionada) * aprendizagem por reforço (baseado em sucesso ou fracasso, recompensa ou penalidade) * redes neurais artificiais (uma das formas mais populares e eficazes de aprendizagem) - ambiente * determinístico (c/ probabilidade, próximo estado = estado anterior + ação) * estocástico (não determinístico, s/ probabilidade) * totalmente, parcialmente ou não observável * episódico ou sequencial * estático ou dinâmico * discreto ou contínuo - agentes (racionais, reativos, baseados em modelo, em objetivo, em utilidade) - algoritmos (de busca, de caminho mais curto, genéticos, etc) - comunicação * processamento de linguagem natural * classificação, busca e extração de informação * tradução (que lida com sintaxe, gramática, etc) * reconhecimento de voz * processamento de imagem - robótica (agentes físicos e seus desafios ligados a movimentação, equilíbrio, etc) - e muitos outros tópicos
Os últimos capítulos do livros são menos técnicos, mais filosóficos, bem interessantes também. Nesse ponto ele fala sobre o impacto da IA na vida das pessoas, por exemplo, sobre a possibilidade de criarmos uma inteligência superior à nossa e inteligente o suficiente para criar de fato uma "ultrainteligência", o que foi chamado de "explosão de inteligência" ou de "singularidade tecnológica". Nessa parte, existem considerações tanto sobre os riscos desses desdobramentos, quanto sobre os benefícios (que já temos hoje, inclusive). Ele descreve que até hoje os programas de IA criaram mais empregos do que eliminaram, criaram empregos mais interessantes e melhores remunerados. Coloca-se também que a sociedade moderna se tornou dependente de computadores em geral e algumas áreas são simplesmente inviáveis apenas com o trabalho humano.
No geral fiquei bastante satisfeito com a leitura do livro, em vários momentos coisas que vi no documentário AlphaGo  passaram a fazer mais sentido pra mim :-D
I had to read this as part of my Artificial Intelligence course in Georgia Tech's Online Master's in Computer Science program, and as an AI textbook it was excellent. It provides detailed, and easy to follow, algorithms ranging from minimax and alpha-beta to Bayes Nets, Hidden Markov Models, A*, Neural Nets, and plenty more. I did not read every page of this book, but I can attest that I would not have done nearly as well in my course without it and if I need to look up an AI algo, I'll turn here first to read what Russell and Norvig have to say first and then check other resources.
Mind-blowing text diving deep into topics with extremely clear explanations. Straddles the line better than any other textbook I think I've ever read. Check out my implementations here: https://github.com/WarrenGreen/AI-Norvig
Wants a book that explains broad and deep AI yet in laymen term (nearly)? This is IT. Of all the AI books I have read, this one is arguably the most accessible to undergrads (CS, EE background) It assumes only minimal mathematical formalities and pretty much the maths things are self-contained. The authors did a great job of keeping the contents up-to-date with the latest happenings in AI, while keeping the readers sane. Overall, thumbs up! I've been reading First 6th Chapters of this book in a Artificial Intelligence course at the university.
I did not finish this text book, but in the early chapter I get some interesting idea.
The latest AI study based on general principles of rational agents, where the concept are: - goal formulation: based on the current situation and the agent’s performance measure - problem formulation: process of deciding what actions and states to consider, given a goal - search: process of looking for a sequence of actions that reaches the goal
The agent can be: Problem solving Agents (Part II, Search) Logical Agents (Part III: Knowledge, Reasoning and Planning) How Agent react with Uncertainty (Part IV: Uncertain, knowledge and reasoning) Learning Agents (Part V: Learning)
Part VI: Communicating, Perceiving and Action (NLP, Perception, Robotics) Implementation part of acting humanly
A search algorithm takes a problem as input and returns a solution in the form of an action sequence. Once a solution is found, the actions it recommends can be carried out. This is called the execution phase
*It is also interesting with the definition of AI
Four Approach of AI: Thinking: Thought Process and Reasoning Acting: Behavior Humanly: Measure success in terms of fidelity to human performance Rationality: Measure against an ideal performance measure
Artificial Intelligence: A Modern Approach by Stuart Russel and Peter Norvig is book introducing the reader to a wide range of AI topics. From the traditional Search problems to Natural Language Processing, this book has it all. It brims with a lot of detail and is suited to anyone with an interest in AI. If you want an introduction to this -sometimes daunting- field, this book is for you.
Even though it doesn’t go in great detail in all topics, it offers a thorough investigation of most of them. The extra detail does not come with extra complications, thankfully, with the more advanced ideas covered in as clean a manner as the more basic ones. There is pseudocode for most of the algorithms (and more is added with each edition) and there are a lot of examples, all of which make understanding and following the concepts so much more easier.
What stands out for this book is how well written it is. Everyone can pick it up with the most basic of backgrounds and they will understand and enjoy it. The prose is clear and at times even playful and colorful. This, on top of the great and simple explanations, makes studying it a joy.
Also, for those who want to get their hands dirty with code, the book has some official accompanying public repositories (https://github.com/aimacode).
If you are interested in a guide in the fast growing field of Artificial Intelligence, look no further than this book. I cannot recommend it enough.
This is the most complete and comprehensive book I read on a subject of Artificial Intelligence so far and it's very well written as well. If you plan diving into AI really seriously and you are keen to invest some good amount of time going through 1000 pages of this book then I really recommend it for you. Great addition to this book is A.I. course https://www.ai-class.com/home/ led by coauthor of this book, Peter Norvig and Sebastian Thrun, a Professor of Computer Science and Electrical Engineering at Stanford University. Last three months I spent every day with both this book and A.I. course and it was the most fascinating learning experience I've ever head.
This book is actually two books, one in computer science and another in statistics, incoherently combined. It does not give a profound view of AI. It touches on it in the first chapter with a lot of unassertive statements, probably because they are dated. It is used widely as a textbook but no longer due to its current size. It is too big and talks about so many subjects that might be justifiably deemed irrelevant to the field of AI. It is also different from what is presented in the works of the founders of AI like McCarthy and Minsky. Although it is a big book, it is not keeping up with major advances in deep learning, data science, and advanced image processing.