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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Nodes labeled by compound databases have sets of successor nodes each labeled by one of the components. These successor nodes are called AND nodes because ...
Nodes labeled by these component databases have successor nodes labeled by the results of rule applications. These successor nodes are called OR nodes ...
A pair of nodes may be successors of each other; in this case the pair of directed arcs is sometimes replaced by an edge. DB / R / R.2 DB2 DB4 R.3 DB3 A ...
A node in the tree having no successors is called a tip node. ... A sequence of nodes (nii, nig,..., nik), with each ni; a successor of ni, -, for j = 2,...
operator that is applied to a node to give all of the successors of that node (and ... We call this process of applying the successor operator to a node, ...
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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