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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goal node . That is , f ( n ) is an estimate of the cost of a minimal cost path
constrained to go through node n . That node on OPEN having the smallest value
of f is then the node estimated to impose the least severe constraint ; hence it is ...
Thus , we have : RESULT 2 : At any time before A * terminates , there exists on
OPEN a node n ' that is on an optimal path from s to a goal node , with f ( n ) = f * (
s ) . Combining this result with our previous argument , that even the smallest f ...
In the initial graphs , match edges between the fact and goal graphs must be
between leaf nodes . After the graphs are extended by B - rule and F - rule
applications , the matches might occur at any literal node . After all possible
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