This book takes the viewpoint that plain symbol processing
techniques have little hope of reproducing the depth and breadth of
capabilities found in human beings. The book introduces new
foundational principles to AI: connectionist/neural networking
methods, case based and memory based methods and picture
processing.

The book looks at methods of AI as different ways of doing pattern
recognition. One way to do pattern recognition is to compare a
problem to stored cases. At the other end of the spectrum,
Classical Symbol Processing AI compresses cases down to a small set
of rules and then works only with this condensed knowledge. In
between these two extremes are neural networks, especially backprop
type networks. As much as possible the book compares these three
basic methods using actual AI programs.

The structure of the book starts at the bottom of human abilities
with vision and other simple pattern recognition abilities and
moves on to the higher levels of problem solving and game playing
and finally to the level of natural language and understanding of
the world. At the higher levels more complex computer architectures
are needed that include methods for structuring thoughts.

The book is organized in a manner in which the reader will get an
intuitive feeling for the principles of AI. Throughout the book
applications of basic principles are demonstrated by examining some
classic AI programs in detail. The book can serve as a text for
juniors, seniors and first year graduate students in Computer
Science or Psychology and includes sample problems and data for
exercises and a list of frequently asked questions.