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. |
From inside the book
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In AI, we are typically not interested in uninformed procedures, but we describe two types here for purposes of comparison: depth-first search and breadth-first search. The first type of uninformed search orders the nodes on OPEN in ...
The search that results from such an ordering is called breadth-first because expansion of nodes in the search tree proceeds along “contours” of equal depth. In Figure 2.7, we show the search tree generated by a breadth-first search in ...
y *28lb s :::ed ::C i|-:odu :r :::|p :ee : ::|tr |h |arc e :S : ::7A :2.:2 'g. i : F. : -: :: : :: : : : : : :|: : : : : :: : s: : : S 2.4. HEURISTIC GRAPH-SEARCH PROCEDURES The uninformed search methods, whether breadth-first.
HEURISTIC GRAPH-SEARCH PROCEDURES The uninformed search methods, whether breadth-first or depth-first, are exhaustive methods for finding paths to a goal node. In principle, these methods provide a solution to the path-finding problem, ...
(If we simply use the evaluation function f(n) = d(n), we get the breadth-first search process.) The choice of evaluation function critically determines search results. The use of an evaluation function that fails to recognize the true ...
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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 |