Preface
Artificial Intelligence (AI) is a big field, and this is a big book. We have tried to explore the
full breadth of the field, which encompasses logic, probability, and continuous mathematics;
perception, reasoning, learning, and action; and everything from microelectronic devices to
robotic planetary explorers. The book is also big because we go into some depth.
The subtitle of this book is "A Modern Approach." The intended meaning of this rather
empty phrase is that we have tried to synthesize what is now known into a common frame-
work, rather than trying to explain each subfield of AI in its own historical context. We
apologize to those whose subfields are, as a result, less recognizable.
New to this edition
This edition captures the changes in Al that have taken place since the last edition in 2003.
There have been important applications of AI technology, such as the widespread deploy-
ment of practical speech recognition, machine translation autonomous vehicles, and house-
hold 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
stare
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 slate 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 theo-
retical footing.
•
We have expanded coverage of Web search and information extraction, and of tech-
niques 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 SO% reflects
older work but has been largely rewritten to present a more unified picture of the field.
vii
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chandlearn2014-09-13almost clear, except some fomulations
