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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HEURISTIC GRAPH - SEARCH PROCEDURES The uninformed search methods
, whether breadth - first or depth - first , are exhaustive methods for finding paths
to a goal node . In principle , these methods provide a solution to the path ...
THE HEURISTIC POWER OF EVALUATION FUNCTIONS The selection of the
heuristic function is crucial in determining the heuristic power of search algorithm
A . Using h = 0 assures admissibility but results in a breadth - first search and is ...
AO * : A HEURISTIC SEARCH PROCEDURE FOR AND / OR GRAPHS As with
ordinary graphs , we define the process of expanding a node as the application
of a successor operator that generates all of the successors of a node ( through
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PRODUCTION SYSTEMS AND AI
SEARCH STRATEGIES FOR
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