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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As we have already mentioned, AI methods and techniques have been applied in several different problem areas. To help motivate our subsequent discussions, ...
This hypothesize-and-test paradigm is applied at many levels of the perception process. Several aligned segments suggest a straight line; a line detector ...
A special detector, applied to the scene, confirms this expectation, thus raising confidence in the window hypothesis. We discuss some of the fundamental ...
If the precondition is satisfied, the rule can be applied. Application of the rule changes the database. The control system chooses which applicable rule ...
It also keeps track of the rules that have been applied so that it can compose them into the sequence representing the problem solution.
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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