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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We see that the problem of finding a sequence of rules transforming one
database into another is equivalent to the problem of finding a path in a graph .
Often it is convenient to assign positive costs to arcs , to represent the cost of
applying the ...
20 we show , by dashed arcs , some of the possible a arcs that the matcher is
permitted to seek . If it can find such an arc , the match is successful . Unless all of
the goal arcs can be matched , the matcher terminates with failure . A system with
Then , we look in the taxonomic hierarchy above each such node Ni to see if Ni
inherits an ai arc to some Skolem ... In the simplest case , the variable goal node ,
say , x , is tied to constant fact nodes , N1 , N2 , . . . , Nk , by arcs labeled al , a2 ...
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