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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Note that the recursive algorithm does not remember all databases that it visited
previously . Backtracking involves " forgetting ” all databases whose paths lead to
failures . The algorithm remembers only those databases on the current path ...
Note that the variable v , in Q ( V , A ) , can be replaced by a new variable , w , but
that neither occurrence of the variable v in the conjuncts of the embedded
conjunction , [ ~ R ( v ) A ~ P ( v ) ] , can be renamed because this variable also
Note that our CANCEL graph method treats conjunctively related goal nodes
correctly . Each conjunct must be proved before the parent is proved .
Disjunctively related fact nodes are treated in a similar manner . In order to use
one member of ...
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