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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... heuristic information, and search procedures using it are called heuristic search methods. It is often possible to specify heuristics that reduce search effort (below that expended by, say, breadth-first search) without sacrificing the ...
... puzzle using this evaluation function are summarized in Figure 2.8. The value off for each node is circled; the uncircled numbers show the order in which nodes are l ~ Oi!'. # = 5 ||Node 2 2 8 73 HEURISTIC GRAPH-SEARCH PROCEDURES.
... ā and h is an estimate of h". An obvious choice for g(n) is the cost of the path in the search tree from s to n given by summing the arc costs encountered while tracing the pointers from 75 HEURISTIC GRAPH-SEARCH PROCEDURES.
... heuristic information from the problem domain. Such information might be similar to that used in the function W(n) in the 8-puzzle example. We call h the heuristic function and will discuss it in more detail later. Suppose we now use as ...
... CLOSED. Therefore, g(n) = gā(n') and f(n) = gā(n") + h(n'). Since we are assuming h (n') < h"(n'), we can write f(n) < gā(n") + h"(n) = f*(n'). But the f* value of any node on an optimal 77 HEURISTIC GRAPH-SEARCH PROCEDURES.
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 |