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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One important method uses a real - valued function over the nodes called an
evaluation function . Evaluation functions have been based on a variety of ideas :
Attempts have been made to define the probability that a node is on the best path
8 A search tree using an evaluation function . expanded . We see that the same
solution path is found here as was found by the other search methods , although
the use of the evaluation function has resulted in substantially fewer nodes being
Specify a nine - dimensional vector w , such that the dot product cow is a useful
evaluation function for use by MAX ( playing Xs ) to evaluate nonterminal
positions . Use this evaluation function to perform a few minimax searches
making any ...
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PRODUCTION SYSTEMS AND AI
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