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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The formalization of the deductive process using the language of predicate logic, for example, helps us to understand more clearly some of the components of ...
Papers describing various applications of AI and logic to database organization and retrieval are contained in a book edited by Gallaire and Minker (1978).
... Shaw, and Simon (1957) to propositional logic. The resolution principle of Robinson (1965) greatly accelerated work on automatic theorem proving.
A collection of papers in a book by Manna and Waldinger (1977) describe logic-based methods for program verification, synthesis, and debugging. 0.3.7.
We discuss this situation later, after introducing an appropriate language (predicate logic) for talking about goals described by conditions.
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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