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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9 GO LOOP This procedure is sufficiently general to encompass a wide variety of
special graph-searching algorithms. The procedure generates an explicit graph,
G, called the search graph and a subset, T, of G called the search tree.
In this case, each member of M is added to OPEN and is installed in the search
tree as a successor of n. The search graph is the search tree throughout the
execution of the algorithm, and there is no need to change parents of the nodes
in T. If ...
graph and search tree shown in Figure 2.4. The dark arrows along certain arcs in
this search graph are the pointers that define parents of nodes in the search tree.
The solid nodes are on CLOSED, and the other nodes are on OPEN at the time ...
UNINFORMED GRAPH-SEARCH PROCEDURES If no heuristic information from
the problem domain is used in ordering ... The first type of uninformed search
orders the nodes on OPEN in descending order of their depth in the search tree.
We prove this result using induction on the depth of a node in the A2 search tree
at termination. First, we prove that if A2 expands a node n having zero depth in its
search tree, then so will A1. But, in this case, n = s. If s is a goal node, neither ...
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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 |