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.
... it is possible that a complete reformulation of the problem (changing the very notion of what a state is, for example) will result in a smaller space.
... produced by applying any applicable rule to D. (c) The database that results ... and applied to that database to produce a result independent of order.
Employing this conversion results in a more complex global database and rule set and in a simpler sort of control regime (irrevocable).
Of course, when a production rule is applied to a component, a database may result that can itself be split. The components of this database are processed ...
Nodes labeled by these component databases have successor nodes labeled by the results of rule applications. These successor nodes are called OR nodes ...
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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