Preface for AI: A Modern Approach
There are many textbooks that offer an introduction to artificial
intelligence (AI). This text has five principal features that together
distinguish it from other texts.
- Unified presentation of the field.
Some texts are organized from a historical perspective, describing
each of the major problems and solutions that have been uncovered in
40 years of AI research. Although there is value to this perspective,
the result is to give the impression of a dozen or so barely related
subfields, each with its own techniques and problems. We have chosen
to present AI as a unified field, working on a common problem in
various guises. This has entailed some reinterpretation of past
research, showing how it fits within a common framework and how it
relates to other work that was historically separate.
It has also led us to include material not normally covered in AI texts.
- Intelligent agent design.
The unifying theme of the book is the concept of an intelligent
agent. In this view, the problem of AI is to describe and build
agents that receive percepts from the environment and perform actions.
Each such agent is implemented by a function that maps percepts to
actions, and we cover different ways to represent these functions,
such as production systems, reactive agents, logical planners, neural
networks, and decision-theoretic systems. We explain the role of
learning as extending the reach of the designer into unknown
environments, and show how it constrains agent design, favoring
explicit knowledge representation and reasoning. We treat robotics and
vision not as independently defined problems, but as occurring in the
service of goal achievement. We stress the importance of the task
environment characteristics in determining the appropriate agent
design.
- Comprehensive and up-to-date coverage.
We cover areas that are sometimes underemphasized, including reasoning
under uncertainty, learning, neural networks, natural language,
vision, robotics, and philosophical foundations.
We cover many of the more recent ideas
in the field, including simulated annealing, memory-bounded search,
global ontologies, dynamic and adaptive probabilistic (Bayesian)
networks, computational learning theory, and reinforcement learning.
We also provide extensive notes and references on the
historical sources and current literature for the main ideas in each
chapter.
- Equal emphasis on theory and practice.
Theory and practice are given equal emphasis. All material is
grounded in first principles with rigorous theoretical analysis where
appropriate, but the point of the theory is to get the concepts across
and explain how they are used in actual, fielded systems. The reader
of this book will come away with an appreciation for the basic
concepts and mathematical methods of AI, and also with an idea of what
can and cannot be done with today's technology, at what cost, and
using what techniques.
- Understanding through implementation.
The principles of intelligent agent design are clarified by using them
to actually build agents. Chapter 2 provides an overview of agent
design, including a basic agent and environment project. Subsequent
chapters include programming exercises that ask the student to add
capabilities to the agent, making it behave more and more
interestingly and (we hope) intelligently. Algorithms are presented
at three levels of detail: prose descriptions and pseudo-code in the
text, and complete Common Lisp programs available
on the Internet. All the agent programs are
interoperable and work in a uniform framework for simulated
environments.
This book is primarily intended for use in an undergraduate course or
course sequence. It can also be used in a graduate-level course
(perhaps with the addition of some of the primary sources suggested in
the bibliographical notes). Because of its comprehensive coverage and
the large number of detailed algorithms, it is useful as a primary
reference volume for AI graduate students and professionals wishing to
branch out beyond their own subfield. We also hope that AI researchers
could benefit from thinking about the unifying approach we advocate.
The only prerequisite is familiarity with basic concepts of computer
science (algorithms, data structures, complexity) at a sophomore
level. Freshman calculus is useful for understanding neural networks
and adaptive probabilistic networks in detail. Some experience with
nonnumeric programming is desirable, but can be picked up in a few
weeks study. We provide implementations of all algorithms in Common
Lisp (see Appendix B), but other languages such as Scheme, Prolog,
Smalltalk, C++, or ML could be used instead.
Overview of the book
The book is divided into eight parts. Part I, ``Artificial Intelligence,''
sets the stage for all the others, and offers a view of the AI
enterprise based around the idea of intelligent agents--systems that
can decide what to do and do it. Part II, ``Problem Solving,''
concentrates on methods for deciding what to do when one needs to
think ahead several steps, for example in navigating across country or
playing chess. Part III, ``Knowledge and Reasoning,'' discusses ways
to represent knowledge about the world--how it works, what it is
currently like, what one's actions might do--and how to reason
logically with that knowledge. Part IV, ``Acting Logically,'' then
discusses how to use these reasoning methods to decide what to do,
particularly by constructing plans. Part V, ``Uncertain
Knowledge and Reasoning,'' is analogous to Parts III and IV, but it
concentrates on reasoning and decision-making in the presence of
uncertainty about the world, as might be faced, for example, by a
system for medical diagnosis and treatment.
Together, Parts II to V describe that part of the intelligent agent
responsible for reaching decisions. Part VI, ``Learning,'' describes
methods for generating the knowledge required by these decision-making
components; it also introduces a new kind of component, the
neural network, and its associated learning procedures. Part VII,
``Communicating, Perceiving, and Acting,'' describes ways in which an
intelligent agent can perceive its environment so as to know what is
going on, whether by vision, touch, hearing, or understanding
language; and ways in which it can turn its plans into real actions,
either as robot motion or as natural language utterances. Finally,
Part VIII, ``Conclusions,'' analyses the past and future of AI, and
provides some light amusement by discussing what AI really is and why
it has already succeeded to some degree, and airing the views of those
philosophers who believe that AI can never succeed at all.