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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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 appropriate that
( These costs model the costs of rule applications ; again we need to assume that
each cost is greater than some small positive number , e . ) The connector costs
can then be used to calculate the cost of a solution graph . Let the cost of a ...
newly computed costs of its successors ) and marks the outgoing connector on
the estimated best “ path ” to terminal nodes . This revised cost estimate is
propagated upward in the graph . ( Acyclicity of our graphs guarantees no loops
in this ...
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