Principles of Artificial Intelligence
A 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.
Results 1-5 of 27
... to answer users' questions about that subject. To take a specific example, suppose the facts are the personnel records of a large corporation. Example items in such a database might be representations for such facts as “Joe Smith works in ...
... works on a problem in a forward direction and one that works in a backward direction, it is often convenient to make this distinction explicit. When a problem has intuitively clear states and goals and when we choose to employ descriptions ...
... (in this case consisting of the symbol M) is enclosed in a double box. Such nodes are called terminal nodes. (We could ... works for a simple hydrocarbon. The system for generating candidate structures can be viewed as a production system ...
... in that it does not accurately appraise the difficulty of exchanging the positions of two adjacent tiles. An estimate that works quite well for the 8-puzzle is h (n) = P(n) + 3S(n). The quantity S(n) is a sequence score obtained by ...
You have reached your viewing limit for this book.
CHAPTER 3 SEARCH STRATEGIES FOR DECOMPOSABLE PRODUCTION SYSTEMS
CHAPTER 4 THE PREDICATE CALCULUS IN AI
CHAPTER 5 RESOLUTION REFUTATION SYSTEMS
CHAPTER 6 RULEBASED DEDUCTION SYSTEMS
CHAPTER 7 BASIC PLANGENERATING SYSTEMS
CHAPTER 8 ADVANCED PLANGENERATING SYSTEMS
CHAPTER 9 STRUCTURED OBJECT REPRESENTATIONS