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.
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The initial global database is this description of the initial problem state. Virtually any kind of data structure can be used to describe states.
These moves are modeled by production rules that operate on the state descriptions in the appropriate manner. The rules each have preconditions that must be ...
We can easily compute the value of this function for any state description. From the initial state, we achieve maximum increase in the value of this ...
For example, suppose the goal state is 1 2 3 74 86 5 and the initial state is 1 2 5 74 86 3 Any applicable rule applied to the initial state description ...
Backing up will occur (a) whenever we generate a state description that already occurs on the path back to the initial state description, (b) whenever we ...
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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