AIMA 3rd edition (Russell, Norvig) available for pre-order now(amazon.com)
amazon.com
AIMA 3rd edition (Russell, Norvig) available for pre-order now
http://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597/
18 comments
I took the liberty to clean up the formatting on that, hope you don't mind:
This edition captures the changes in AI that have taken place since the last edition in 2003. There have been important applications of AI technology, such as the widespread deployment of practical speech recognition, machine translation, autonomous vehicles, and household robotics. There have been algorithmic landmarks, such as the solution of the game of checkers. And there has been a great deal of theoretical progress, particularly in areas such as probabilistic reasoning, machine learning, and computer vision. Most important from our point of view is the continued evolution in how we think about the field, and thus how we organize the book. The major changes are as follows:
- We place more emphasis on partially observable and nondeterministic environments, especially in the nonprobabilistic settings of search and planning. The concepts of belief state (a set of possible worlds) and state estimation (maintaining the belief state) are introduced in these settings; later in the book, we add probabilities.
- In addition to discussing the types of environments and types of agents, we now cover in more depth the types of representations that an agent can use. We distinguish among atomic representations (in which each state of the world is treated as a black box), factored representations (in which a state is a set of attribute/value pairs), and structured representations (in which the world consists of objects and relations between them).
- Our coverage of planning goes into more depth on contingent planning in partially observable environments and includes a new approach to hierarchical planning.
- We have added new material on first-order probabilistic models, including open-universe models for cases where there is uncertainty as to what objects exist.
- We have completely rewritten the introductory machine-learning chapter, stressing a wider variety of more modern learning algorithms and placing them on a firmer theoretical footing.
- We have expanded coverage of Web search and information extraction, and of techniques for learning from very large data sets.
- 20% of the citations in this edition are to works published after 2003.
- We estimate that about 20% of the material is brand new. The remaining 80% reflects older work but has been largely rewritten to present a more unified picture of the field.
This edition captures the changes in AI that have taken place since the last edition in 2003. There have been important applications of AI technology, such as the widespread deployment of practical speech recognition, machine translation, autonomous vehicles, and household robotics. There have been algorithmic landmarks, such as the solution of the game of checkers. And there has been a great deal of theoretical progress, particularly in areas such as probabilistic reasoning, machine learning, and computer vision. Most important from our point of view is the continued evolution in how we think about the field, and thus how we organize the book. The major changes are as follows:
- We place more emphasis on partially observable and nondeterministic environments, especially in the nonprobabilistic settings of search and planning. The concepts of belief state (a set of possible worlds) and state estimation (maintaining the belief state) are introduced in these settings; later in the book, we add probabilities.
- In addition to discussing the types of environments and types of agents, we now cover in more depth the types of representations that an agent can use. We distinguish among atomic representations (in which each state of the world is treated as a black box), factored representations (in which a state is a set of attribute/value pairs), and structured representations (in which the world consists of objects and relations between them).
- Our coverage of planning goes into more depth on contingent planning in partially observable environments and includes a new approach to hierarchical planning.
- We have added new material on first-order probabilistic models, including open-universe models for cases where there is uncertainty as to what objects exist.
- We have completely rewritten the introductory machine-learning chapter, stressing a wider variety of more modern learning algorithms and placing them on a firmer theoretical footing.
- We have expanded coverage of Web search and information extraction, and of techniques for learning from very large data sets.
- 20% of the citations in this edition are to works published after 2003.
- We estimate that about 20% of the material is brand new. The remaining 80% reflects older work but has been largely rewritten to present a more unified picture of the field.
To ask a somewhat nuanced question, what do you feel the modern relevance of Lisp and Prolog are in AI? After writing a great deal about both language families, your first "go-to" language these days seems to be Python. Have major features for exploratory programming historically associated with Lisp been incorporated into dynamic/scripting languages such as Python, Ruby, and Lua?
I think that when I was in grad school, Lisp was unique in the power it brought for the type of exploratory programming that was necessary for AI. I think that today Lisp is still a great choice, but there are other choices that are also good---as you say, other languages have incorporated many (but not all) of the good parts of Lisp, so that today the choice of language can be made based on other factors.: for example, what language do you already know, do your friends know, etc.
There is a lot of content in an AI course, and I didn't think it made sense for an instructor to take a week or two out of the semester to teach Lisp, so we added Java and Python support. Java because it is widely-known, and Python because it is fairly widely-known and because, of all the languages I know, it happens to be closest to the pseudocode we invented in the book.
I never programmed at a serious level in Prolog, so I'll let other people comment on that.
There is a lot of content in an AI course, and I didn't think it made sense for an instructor to take a week or two out of the semester to teach Lisp, so we added Java and Python support. Java because it is widely-known, and Python because it is fairly widely-known and because, of all the languages I know, it happens to be closest to the pseudocode we invented in the book.
I never programmed at a serious level in Prolog, so I'll let other people comment on that.
Do you plan on releasing the book as an ebook anywhere? Such as the kindle or any other form?
That choice is up to the publisher -- the authors have no control. Pearson has traditionally released eBooks, but so far not in Kindle format.
There used to be an "I'd like to read this book on kindle" option on Amazon to request an ebook edition from the publisher.
In fact I'm sure that option was available a few days ago, but now it seems to have disappeared...
However halfway down the page it does say "If you are a publisher or author and hold the digital rights to a book, you can sell a digital version of it in our Kindle Store."
In fact I'm sure that option was available a few days ago, but now it seems to have disappeared...
However halfway down the page it does say "If you are a publisher or author and hold the digital rights to a book, you can sell a digital version of it in our Kindle Store."
Is there going to be any mention of the recent developments in researching Artificial General Intelligence (http://www.cis.temple.edu/~pwang/Writing/AGI-Intro.html) as opposed to GOFAI and narrow AI?
there has been a great deal of theoretical progress, particularly in areas such as probabilistic reasoning, machine learning
Those are wide fields, are there any breakthroughs that really stand out in this decade?
Those are wide fields, are there any breakthroughs that really stand out in this decade?
Does the book discuss the Markov logic network (MLN) formulation for structured representations of belief states? In your opinion, how promising is the MLN approach? Thank you!
Ah very nice, the chess position shown on the cover appears to be the final position from game 6 of the 1997 Kasparov vs. Deep Blue match.
http://www.amazon.com/gp/product/images/0136042597/ref=dp_im...
http://en.wikipedia.org/wiki/Deep_Blue_%E2%80%93_Kasparov,_1...
http://www.amazon.com/gp/product/images/0136042597/ref=dp_im...
http://en.wikipedia.org/wiki/Deep_Blue_%E2%80%93_Kasparov,_1...
If anyone is looking for a good reference to one of AI's subtopics--Machine Learning--then I highly recommend Christopher Bishop's Pattern Recognition and Machine Learning.
I believe the book was published in 2006, so a vast majority of the material is cutting edge. It's a difficult read, and not really meant for the duct tape programmer. But if you have the patience to stick with this book for as long as I have (an entire year), then you'll be well positioned to tackle any problem in Artificial Intelligence.
I believe the book was published in 2006, so a vast majority of the material is cutting edge. It's a difficult read, and not really meant for the duct tape programmer. But if you have the patience to stick with this book for as long as I have (an entire year), then you'll be well positioned to tackle any problem in Artificial Intelligence.
does anyone know what are the differences from the 2nd edition?
Will it be available and optimized for the Kindle DX?
Well, it's a good thing I didn't spend $100 on the 2nd edition last month at the book store.
Any plans on an e-book of some format (pdf, mobi, azw) or otherwise?
This edition captures the changes in AI that have taken place since the last edition in 2003. There have been important applications of AI technology, such as the widespread deployment of practical speech recognition, machine translation, autonomous vehicles, and household robotics. There have been algorithmic landmarks, such as the solution of the game of checkers. And there has been a great deal of theoretical progress, particularly in areas such as probabilistic reasoning, machine learning, and computer vision. Most important from our point of view is the continued evolution in how we think about the field, and thus how we organize the book. The major changes are as follows: \begin{itemize} \item We place more emphasis on partially observable and nondeterministic environments, especially in the nonprobabilistic settings of search and planning. The concepts of {\em belief state} (a set of possible worlds) and {\em state estimation} (maintaining the belief state) are introduced in these settings; later in the book, we add probabilities. \item In addition to discussing the types of environments and types of agents, we now cover in more depth the types of {\em representations} that an agent can use. We distinguish among {\em atomic} representations (in which each state of the world is treated as a black box), {\em factored} representations (in which a state is a set of attribute/value pairs), and {\em structured} representations (in which the world consists of objects and relations between them). \item Our coverage of planning goes into more depth on contingent planning in partially observable environments and includes a new approach to hierarchical planning. \item We have added new material on first-order probabilistic models, including {\em open-universe} models for cases where there is uncertainty as to what objects exist. \item We have completely rewritten the introductory machine-learning chapter, stressing a wider varie\ ty of more modern learning algorithms and placing them on a firmer theoretical footing. \item We have expanded coverage of Web search and information extraction, and of techniques for learning from very large data sets. \item 20\% of the citations in this edition are to works published after 2003. \item We estimate that about 20\% of the material is brand new. The remaining 80\% reflects older work but has been largely rewritten to present a more unified picture of the field. \end{itemize}