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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In our discussions of graph-search strategies, we speak as if the various databases produced by rule applications are actually represented, each in its entirety, as nodes in a graph or tree. Because these databases are usually very ...
A graph may be specified either explicitly or implicitly. In an explicit specification, the nodes and arcs (with associated costs) are explicitly given by a table. The table might list every node in the graph, its successors, and the ...
Expanding s, the successors of s, ad infinitum, makes explicit the graph that is implicitly defined by s and the ... a pointer to n from those members of M that were not already in G (i.e., not already on either OPEN or CLOSED).
M already on CLOSED, decide for each of its descendants in G whether or not to redirect its pointer. (See text.) ... The procedure generates an explicit graph, G, called the search graph and a subset, T, of G called the search tree.
Thus, in this special case, the members of M in steps 6 and 7 are not already on either OPEN or CLOSED. ... The search graph is the search tree throughout the execution of the algorithm, and there is no need to change parents of the ...
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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