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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Each rule has a precondition that is either satisfied or not by the global database .
If the precondition is satisfied , the rule can be applied . Application of the rule
changes the database . The control system chooses which applicable rule should
When these conditions are satisfied , a production system improves its efficiency
by avoiding needless exploration of redundant solution paths that are all
equivalent except for rule ordering . In Figure 1 . 8 we have three rules , R1 , R2 ,
and R3 ...
When the monotone restriction is not satisfied , it is possible that some node has
a smaller f value at expansion than that of a previously expanded node . We can
exploit this observation to improve the efficiency of A * under this condition .
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