Please find below suggestions for some textbooks which I found most relevant
for understanding and modeling intelligent behaviour in general, and for
developing the AIXI model in particular. If you are confused by the amount, diversity or
complexity of the references below, I suggest you to start with the
Reinforcement Learning book by Sutton and Barto. It requires no background knowledge,
describes the key ideas, open problems, and great applications of this field.
Don't be surprised about the ease of the book,
it teaches understanding, not proofs.
It gets really tough to make things work in practice and to prove things.
The
Artificial Intelligence book by Russell and Norvig gives a comprehensive overview over
AI in general.
The Kolmogorov Complexity book by Li and Vitanyi
is an excellent introduction to algorithmic information theory.
If you have some background knowledge in decision theory and algorithmic information theory you
may be interested in the Theory of Universal Artificial Intelligence.
Long AI Reader's Guide
For the impatient.
If you are the sort of impatient student who wants to build super
intelligent machines right away without "wasting" time reading or
learning too much, well, others have tried in the last 50 years and
failed, and so will you. If you can't hold back, at least read Legg (2008)
[Leg08].
This is an excellently written non-technical thesis on
the necessary ingredients for super intelligent machines.
It will not help you much building one, since in order to
properly understand the general theory and
to bridge the gap to "narrow" but practical existing AI
algorithms, you need a lot more background. Nevertheless,
[Leg08] might motivate you to consider reading the books
I'll recommend now.
Artificial Intelligence.
Russell and Norvig (2003) [RN03]
is the textbook to learn about
Artificial Intelligence. The book gives a broad introduction,
survey, and solid background of all aspects of AI. There is no real
alternative. Whatever subarea of AI you specialize later, you should
understand all introduced concepts, and have implemented and solved
at least some of the exercises.
The textbooks below are relevant for understanding and modeling
general intelligent behavior. If you already got attracted to some
specific AI applications, they may not be relevant for you.
One axis of categorizing AI is into
(1) logical (2) planning and (3) learning aspects.
CSL@ANU has experts in all 3 areas.
Historically, AI research started with (1) in the 1950th, which is
still relevant for many concrete practical applications.
Since at least in humans, high-level logical reasoning seems to
emerge from the more basic learning and planning aspects,
it is conceivable that (1) will play no fundamental role in a general AI system.
So I will concentrate on (2) and (3).
If put together, learning+planning under uncertainty is mainly the domain
of reinforcement learning (RL), also called adaptive control
or sequential decision theory in other fields.
Reinforcement Learning.
Sutton and Barto (1998) [SB98]
is the excellent default RL textbook. It
requires no background knowledge, describes the key ideas, open
problems, and great applications of this field. Don't be surprised
about the ease of the book, it teaches understanding, not proofs. It
gets really tough to make things work in practice or to prove
things [BT96].
If you want to bring order into the bunch of methods and ideas
you've learned so far, and want to understand more deeply their
connection either for curiosity or to extend the existing systems to
more general and powerful ones, you need to learn about some
concepts that at first seem quite disconnected and theoretical.
Information theory.
Intelligence has a lot to do with information processing. Algorithmic
information theory (AIT) is a branch of information theory that is
powerful enough to serve as a foundation for intelligent information
processing. It can deal with key aspects of intelligence, like
similarity, creativity, analogical reasoning, and generalization,
which are fundamentally connected to the induction problem and
Ockham's razor principle.
Li and Vitanyi's (1997) AIT book [LV97]
provides an excellent introduction. Kolmogorov complexity, Minimal Description Length,
universal Solomonoff induction, universal Levin search, and all
that. It requires a background in theoretical computer science in
general and computability theory in particular, which can be
obtained from the classic textbook
[HMU01].
Universal AI.
Now you are in a position to read [Hut05].
The book develops a sound and complete mathematical theory of an optimal
"intelligent" general-purpose learning agent. The theory is complete in
the sense that it gives a complete description of this agent, not
just an incomplete framework with gaps to be filled. But be warned,
it is only a theory. Like it is a long way from e.g. the minimax theory
of optimally playing games like chess to real chess programs, it is
a long way from this theory to a practical general purpose intelligent agent
[VNHS09].
More.
The other recommended books below can be regarded as further readings
that provide more background and deepen your understanding of various
important aspects in AI research.
Bishop (2006) [Bis06
is the excellent default textbook in statistical
machine learning, and should be put on your reading list. Some
Bayesian probability book will be useful too
[Pre02,
Jay03].
Alchin (2006)
[Alc06]
gently
and broadly introduces you to philosophy of science in general and
Earman (1992) [Ear92]
to the induction problem in particular.
J. Veness and K. S. Ng and M. Hutter and D. Silver A Monte Carlo AIXI Approximation Technical Report, arXiv 0909.0801 (2009) 1-42 [for the practically inclined]
Algorithmic information theory, Kolmogorov complexity, Minimal Description Length,
universal Solomonoff induction, universal Levin search, and all that