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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What we mean here by automatic programming might be described as a “supercompiler,” or a program that could take in a very high-level description of what ...
This model might consist of a high-level description such as “A hill with a tree on top with cattle grazing.” The point of the whole perception process is ...
Various rules of inference, including resolution, are described. Systems for proving theorems using resolution are discussed in chapter 5.
One of the first successful AI systems for understanding limited fragments of natural language is described in a book by Winograd (1972).
interface systems for subsets of natural language are described in an article edited by Waltz (1977). Proceedings of biannual conferences on Theoretical ...
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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