Principles of Artificial Intelligence
Morgan Kaufmann, Jun 28, 2014 - Computers - 476 pages
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.
From inside the book
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As a short example of what we mean by an AI production system, we shall illustrate how one is used to solve a simple puzzle. 1.1.1. THE 8-PUZZLE Many AI applications involve composing a sequence of operations. Controlling the actions of ...
For the 8-puzzle and certain other problems, we can easily identify elements of the problem that correspond to these three components. These elements are the problem states, moves, and goal. In the 8-puzzle, each tile configuration is a ...
The 8-puzzle is conveniently interpreted as having the following four moves: Move empty space (blank) to the left, move blank up, move blank to the right, and move blank down. These moves are modeled by production rules that operate on ...
Applying hill-climbing to the 8-puzzle we might use, as a function of the state description, the negative of the number of tiles “out of place,” as compared to the goal state description. For example, the value of this function for the ...
For the instance of the 8-puzzle in Figure 12, the hill-climbing strategy allowed us to find a path to a goal state. In general, however, hill-climbing functions can have multiple local maxima, which frustrates hill-climbing methods.
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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