

AIMA 3rd edition (Russell, Norvig) available for pre-order now - jsrn
http://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597/

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pnorvig
A revised web site should be up shortly, detailing what's new. Here's what the
preface says:

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}

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silentbicycle
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?

~~~
pnorvig
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.

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jah
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://www.amazon.com/gp/product/images/0136042597/ref=dp_image_0?ie=UTF8&n=283155&s=books)

[http://en.wikipedia.org/wiki/Deep_Blue_%E2%80%93_Kasparov,_1...](http://en.wikipedia.org/wiki/Deep_Blue_%E2%80%93_Kasparov,_1997,_Game_6)

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physcab
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.

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osipov
does anyone know what are the differences from the 2nd edition?

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gcheong
Will it be available and optimized for the Kindle DX?

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dmix
Well, it's a good thing I didn't spend $100 on the 2nd edition last month at
the book store.

~~~
mahmud
The 3rd Ed. was announced, IIRC, over a year ago, and sample chapters have
been released.

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holdenk
Any plans on an e-book of some format (pdf, mobi, azw) or otherwise?

