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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... structure of the 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 ...
... structural descriptions of complex organic chemicals from their mass spectrograms and related data [Buchanan and Feigenbaum (1978)]. Other important expert systems are those of Sussman and Stallman (1975) [see also Stallman and Sussman ...
... structure used by an AI production system. Depending on the application, this database may be as simple as a small matrix of numbers or as complex as a large, relational, indexed file structure. (The reader should not confuse the phrase ...
... structure and conventional computational systems that use hierarchically organized programs. The global database can ... structures, and subroutine organization. The production system design is much more modular, and changes to the ...
... structure can be used to describe states. These include symbol strings, vectors, sets, arrays, trees, and lists. Sometimes, as in the 8-puzzle, the form of the data structure bears a close resemblance to some physical property of 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 |