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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... path constrained to go through node n . That node on OPEN having the smallest value of ƒ is then the node estimated ... optimal path from n to a goal . ( The function h * is undefined for any node n that has no accessible goal node ...
... shortest path in the implicit graph being searched from s to any node n in the search tree produced by A * . Then since the cost of each arc in the graph is at least some small positive number e , g * ( n ) ≥ d * ( n ) e . ( Recall ...
... path is equal to f * ( s ) , the minimal cost , and therefore ƒ ( n ' ) ≤ f * ( s ) . Thus , we have : RESULT 2 : At any time before A * terminates , there exists on OPEN a node n ' that is on an optimal path from s to a goal node ...
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