Principles of Artificial Intelligence
A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, "Principles of Artificial Intelligence" describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of the control strategies used.
"Principles of Artificial Intelligence"evolved from the author's courses and seminars at Stanford University and University of Massachusetts, Amherst, and is suitable for text use in a senior or graduate AI course, or for individual study.
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The initial database corresponds to a distinguished node in the graph called the
start node . The start node has an outgoing connector to a set of successor nodes
corresponding to the components of the initial database ( if it can be ...
Nodes corresponding to MIN ' s next move have successors that are like AND
nodes . From MAX ' s point of view , a solution ( that is , a win ) must be
obtainable from all of these successors . Nodes corresponding to MAX ' s next
move have ...
For F - rule programs , the pattern is the antecedent of the corresponding rule ; for
B - rule programs , the pattern is the consequent . For simplicity , we assume that
a pattern consists of a single literal only . Patterns can contain ? - variables ...
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