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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... representing contextual knowledge and certain techniques for making inferences from that knowledge. Although we do ... represented in the subject domain database. Common knowledge (typically omitted in the subject domain database) is ...
... represented and used is one of the system design problems that invites the methods of Artificial Intelligence. 0.1.3 ... represent and use the knowledge that human experts in these subjects obviously possess and use. This problem is made ...
... represent the scene by some appropriate model. This model might consist of a high-level description such as “A hill with a tree on top with cattle grazing.” The point of the whole perception process is to produce a condensed ...
... 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 other problems, we can easily identify elements of the problem that ...
... representing those states that can be reached by just one move from the initial state. A graph-search control ... represented appropriately, have trivially small state spaces. Sometimes a given state space can be collapsed by recognizing ...
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 |