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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... of the deductive process using the language of predicate logic, for example, helps us to understand more clearly some of the components of reasoning.
These are processed by detectors that search for primitive picture components such as line segments, simple curves, corners, etc.
... of a scene to develop hypotheses about the components of a description. ... then tested by detectors that are specialized to the component descriptions.
The PSI project of Green (1976) includes several components, one of which is a rule-based system for synthesizing programs from descriptions of abstract ...
CHAPTER 1 PRODUCTION SYSTEMS AND AI Most AI systems display a more or less rigid separation between the standard computational components of data, ...
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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