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 task 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 ...
In chapters 7 and 8, we present methods for synthesizing sequences of actions that achieve prescribed goals. These methods are illustrated by considering ...
Wos and his co-workers have achieved excellent results with resolution-based systems [McCharen et al. (1976); Winker and Wos (1978); Winker (1979)].
We can also deal with problems for which the goal is to achieve any one of an explicit list of problem states. A further generalization is to specify some ...
From the initial state, we achieve maximum increase in the value of this function by moving the blank up, so our production system selects the corresponding ...
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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