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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... logic, for example, helps us to understand more clearly some of the components of reasoning. Many informal tasks, including medical diagnosis and information retrieval, can be formalized as theorem-proving problems. For these reasons ...
... logic to database organization and retrieval are contained in a book edited by Gallaire and Minker (1978). The article edited by Waltz (1977) contains several descriptions of systems for querying databases using simplified natural ...
... logic. The resolution principle of Robinson (1965) greatly accelerated work on automatic theorem proving. Resolution theorem proving is thoroughly explained in books by Chang and Lee (1973), Loveland (1978), and Robinson (1979). Bledsoe ...
... logic-based methods for program verification, synthesis, and debugging. 0.3.7. COMBINATORIAL AND SCHEDULING PROBLEMS Scheduling problems are usually studied in operations research. Good general references are the books by Wagner (1975) ...
... logic) for talking about goals described by conditions. Initial The president of the new company approves the sale 1 This sequence of rules replaces terminal symbols by non-terminal symbols. DET N P DET A N V DET N | i DNP P DNP VP ...
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 |