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 Intelligenceevolved 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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... restriction on h , when A * selects a node for expansion it has already found an optimal path to that node . Thus , with this restriction , there is ... monotone restriction , we have that g * 82 SEARCH STRATEGIES FOR AI PRODUCTION SYSTEMS.
... monotone restriction is satisfied , then A * has already found an optimal path to any node it selects for expansion . That is , if A * selects n for expansion , and if the monotone restriction is satisfied , g ( n ) = g * ( n ) . The ...
... monotone restriction is satisfied , the ƒ values of the sequence of nodes expanded by A * is nondecreasing . When the monotone restriction is not satisfied , it is possible that some node has a smaller ƒ value at expansion than that of ...
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