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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... literal node , n , of the graph , we add a new descendant of node n , labeled by the matching goal literal , to the graph . This descendant is called a goal node . Goal nodes ... nodes . ( At termination , the system has essentially inferred ...
... literal nodes labeled by R ( A ) and Q ( x ) . If the same rule is applied more than once , it is important that ... literal , L , unifies with a literal L ' labeling a literal node , n , of the graph , we can add a match arc ( labeled ...
... literal nodes . One way to represent a resolution operation performed between two goal clauses is to connect a literal in one partial solution graph with a complementary literal in another ( as we have done in Figure 6.24 ) . We take ...
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