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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... natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, intelligent data retrieval systems, etc.
An abstract understanding of the basic ideas should facilitate understanding specific AI systems (including strengths and weaknesses) and should also prove ...
0.1.4, THEOREM PROVING Finding a proof (or disproof) for a conjectured theorem in ... lemmas should be proved first in order to help prove the main theorem.
Several automatic theorem proving programs have been developed that possess some of these same skills to a limited degree. The study of theorem proving has ...
... of automatically writing a program to achieve a stated result is closely related to the task of proving that a given program achieves a stated result.
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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