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.
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}
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.