this installment, I would like to draw your attention to Nils Nilsson's
new book, Artificial Intelligence: A New Synthesis (Morgan Kauffman Publishers,
Inc. 1998, ISBN 1-55860-467-7). Here is the blurb, describing the book,
from its back cover:
agents are employed as the central characters in this new introductory
text. Beginning with elementary reactive agents, Nilsson gradually increases
their cognitive horsepower to illustrate the most important and lasting
ideas in AI. Neural networks, genetic programming, computer vision, heuristic
search, knowledge representation and reasoning, Bayes networks, planning,
and language understanding are each revealed through the growing capabilities
of these agents. The book provides a refreshing and motivating new synthesis
of the field by one of AI's master expositors and leading researchers.
Artificial Intelligence: A New Synthesis takes the reader on a complete
tour of this intriguing new world of AI.
While it is not necessarily the intenion of this column to provide book
reviews, I feel that the new synthesis offered by Nilsson is worth examining,
especially in what it lends to a coherent approach to learning the seemingly
disparate topics of AI. Several years ago, I remember Nilsson saying,
Just as Los Angeles has been called "twelve suburbs in search
of a city," AI might be called "twelve topics in search of a
So, how does one synthesize Search, Representation, Reasoning, Vision,
Planning, Leanrning, Uncertainty, Natural Language, Robotics, Game Playing,
Expert Systems, and Lisp/Prolog into a coherent one-semester offering
for beginners? This is not the first time a synthesis has been attempted.
Russell & Norvig's extensive text takes an agent-oriented approach.
It is now considered a classic and currently undergoing evolution into
a second edition. In a straw poll, of about 80 AI instructors, opinions
were gathered about what they considered the "core" fields of
AI that should be included in an introductory course. The responses, categorized
themselevs into two classes, defined by the nature of the course itself.
In programs where the focus was to get students into industry jobs, the
faculty suggested the following topics: Knowledge Representation, Search,
Rule-based systems, Planning/Scheduling, Neural Networks, and Fuzzy Logic.
On the other hand, the consensus among faculty at other universities was
to include: Knowledge Representation, Logic, Search, Game Playing, Reasoning
with Uncertainty, Learning, and Natural Language Understanding. Depending
upon the focus and the specific topics chosen, one is still confronted
with coming up with a coherent story that would tie-in all the topics.
Then there is the issue of the amount of programming, and the choice of
In his new text, Nilsson takes an "evolutionary" approach. He
presents ideas and topics in the context of synthesizing progressively
more complex and competitive agents starting with simple stimulus-response
agents to agents that plan, to agents that reason, and to agents that
communicate. Along the way, one encounters all of the twelve topics mentioned
above. You will find topics embedded in the most unexpected places due
to the new systhesis. For example, you will learn about production systems
in the context of stimulus-response agents. And, production systems form
a natural lead into threshold logic units with lead directly into neural
networks. Thus, one finds studying neural networks in the third chapter
of the book.
surprises, for those already familiar with concepts in AI, are to be discovered
throught the book. Another refreshing aspect of the book is its treatment
of Learning. Learning is not necessariliy treated as a separate topic,
rather it is embedded throughout the book. Hence, the discussion of neural
networks in the context of reactive agents: agents that learn to respond
to stimulii. This approach to presenting the topic of learning is refreshing
and provides the reader with a good context to appreciate the concepts.
The book tries to balance theory with practice, concentrating more on
the core ideas within each topic. Where necessary, formalisms are presented
(with the clarity and crispness we are familiar with in Nilsson's earlier
books), supported by proofs, as well as algorithms for implementation.
One will not find any programs or tutorials to any specific programming
paradigm or language. Depending upon one's own AI background and biases,
one may desire more depth on some of the topics. However, the book is
designed for a fifteen-week semester. It succeeds in providing a crisp,
comprehensive, and up-to-date presentation of the core ideas in AI without
bulking into an encyclopedic volume.
There are WWW resources provided by the publisher for instructors wishing
to use the book in their courses. These include copies of all figures,
solutions to exercises, and an online discussion forum. For specific courses
currently using this book, see the following URLs:
at Bryn Mawr College:
at Swarthmore College: