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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Let R [ Q ; Fu ] be the regression of a literal Q through a ground instance Fu of an
F - rule with precondition , P , delete list , D , and add list , A . Then , if Qu is a
literal in Au , RIQ ; Fu ) = T ( True ) else , if Qu is a literal in Du , R [ Q ; Fu ] = F (
False ) ...
The requirements on P are that its preconditions must regress through F - rule 1
to conditions that match the initial state description and that CLEAR ( A ) regress
through P unchanged ( so that it can be achieved by F - rule 1 ) . The structure of
Its subgoal , HOLDING ( C ) , regresses through unstack ( CA ) to T . Furthermore
, all of the conditions of node 2 ( except HANDEMPTY , which is achieved by
putdown ( C ) ] regress unchanged through putdown ( C ) . Now , we can
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