Principles of Artificial IntelligenceA 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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Selecting rules and keeping track of those sequences of rules already tried and
the databases they produced ... Efficient control strategies require enough
knowledge about the problem being solved so that the rule selected in step 4 has
a ...
A rule is selected, and if it doesn't lead to a solution, the intervening steps are “
forgotten,” and another rule is selected instead. ... strategy can be used
regardless of how much or how little knowledge is available to bear on rule
selection.
The procedure orders the nodes on OPEN in step 8 so that the “best” of these is
selected for expansion in step 4. This ordering can be based on a variety of
heuristic ideas (discussed below) or on various arbitrary criteria. Whenever the
node ...
The nodes are labeled with their corresponding databases and are numbered in
the order in which they are selected for expansion. We assume a depth bound of
five. The dark path shows a solution involving five rule applications. We see that ...
RESULT 3 has an interesting corollary, namely, that any node, n, on OPEN with f(
n) < f*(s) will eventually be selected for expansion by A*. We leave the proof as
an exercise for the reader. Now it is a simple matter to show that A* is admissible.
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Contents
1 | |
17 | |
53 | |
CHAPTER 3 SEARCH STRATEGIES FOR DECOMPOSABLE PRODUCTION SYSTEMS | 99 |
CHAPTER 4 THE PREDICATE CALCULUS IN AI | 131 |
CHAPTER 5 RESOLUTION REFUTATION SYSTEMS | 161 |
CHAPTER 6 RULEBASED DEDUCTION SYSTEMS | 193 |
CHAPTER 7 BASIC PLANGENERATING SYSTEMS | 275 |
CHAPTER 8 ADVANCED PLANGENERATING SYSTEMS | 321 |
CHAPTER 9 STRUCTURED OBJECT REPRESENTATIONS | 361 |
PROSPECTUS | 417 |
BIBLIOGRAPHY | 429 |
AUTHOR INDEX | 467 |
SUBJECT INDEX | 471 |