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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Production Systems for Resolution Refutations 163 5.2. Control Strategies for Resolution Methods 164 5.3. Simplification Strategies 172 5.4.
In chapter 1, we introduce a generalized production system and emphasize its importance as a basic building block of AI systems.
CHAPTER 1 PRODUCTION SYSTEMS AND AI Most AI systems display a more or less rigid separation between the standard computational components of data, ...
There are several differences between this production system structure and conventional computational systems that use hierarchically organized programs.
To solve a problem using a production system, we must specify the global database, the rules, and the control strategy. Transforming a problem statement ...
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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