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 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.
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... labeled by a compound database has a set of AND successor nodes , each labeling one of the component databases . In the other case , a parent node labeled by a component database has a set of OR successor nodes , each labeling the ...
... labeling a literal node , n , of the graph , we can add a match arc ( labeled by the mgu ) directed from node n to a new descendant goal node labeled by L. The same goal literal can be used a number of times , creating multiple goal ...
... labeled by L ' that unifies with L. The result of applying the rule is to add a match arc from the node labeled by L ' to a new descendant node labeled by L. This new node is the root node of the AND / OR graph representation of Wu ...
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