Principles of Artificial IntelligenceA 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. |
From inside the book
Results 1-3 of 31
... expanded by A ,, it was also expanded by A ,. Thus , A , always expands at least as many nodes as does the more informed A ,. We prove this result using induction on the depth of a node in the A , search tree at termination . First , we ...
... expanded . ( Node n , is not on CLOSED either , because we are assuming that it has not been expanded yet . ) Then , if n , is expanded immediately after n1 , it must have been added to OPEN by the process of expanding n ,. Therefore ...
... expansion than that of a previously expanded node . We can exploit this observation to improve the effi- ciency of A * under this condition . By RESULT 5 , when node n is expanded , ƒ ( n ) ≤ƒ * ( s ) . Suppose , during the execution ...
Contents
PROLOGUE | 1 |
PRODUCTION SYSTEMS AND AI | 17 |
SEARCH STRATEGIES FOR | 53 |
Copyright | |
10 other sections not shown