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 solid nodes are on CLOSED , and the other nodes are on OPEN at the time
the algorithm selects node 1 for expansion . ( We assume unit arc costs . ) When
node 1 is expanded , its single successor , node 2 , is generated . But node 2 ...
( This path is the lowest cost path from s to n found so far by the search algorithm
. The value of g ( n ) for certain nodes may decrease if the search tree is altered in
step 7 . ) Notice that this definition implies 8 ( n ) > g * ( n ) . For the estimate h ( n )
We would expect intuitively that the more informed algorithm typically would need
to expand fewer nodes to find a minimal cost path . In the case of the 8 - puzzle ,
this observation is supported by comparing Figure 2 . 7 with Figure 2 . 8 .
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