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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Systems for proving theorems using resolution are discussed in chapter 5. We indicate how several different kinds of problems can be posed as ...
Nilsson's (1971) book on problem solving in AI concentrates on search methods and applications of resolution theorem proving. An introductory book by ...
... mathematical formula manipulation system [Martin and Fateman (1971)]. 0.3.4. THEOREM PROVING Early applications of AI ideas to proving 12 PROLOGUE.
THEOREM PROVING Early applications of AI ideas to proving theorems were made by Gelernter (1959) to plane geometry; and by Newell, Shaw, and Simon (1957) to ...
Pioneering papers by Waldinger and Lee (1969) and by Green (1969a) showed how small programs could be synthesized using theorem-proving techniques.
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CHAPTER 2 SEARCH STRATEGIES FOR AI PRODUCTION SYSTEMS
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