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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... representations and inference processes later in the book. 0.2. OVERVIEW The book is divided into nine chapters and a prospectus. In chapter 1, we introduce a generalized production system and emphasize its importance as a basic ...
... representation itself is used to aid retrieval processes and to make certain common deductions more immediate. Two examples are semantic networks and the so-called frame-based representations. Our point of view toward such representations ...
... representation problem in AI. Usually there are several ways to so represent a problem. Selecting a good representation is one of the important arts involved in applying AI techniques to practical problems. For the 8-puzzle and certain ...
... REPRESENTATION Efficient problem solution requires more than an efficient control strategy. It requires selecting good representations for problem states, moves, and goal conditions. The representation of a problem has a great influence ...
... representations are still poorly understood. It seems that desirable shifts in a problem's representation depend on experience gained in attempts to solve it in a given representation. This experience allows us to recognize the ...
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 |