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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... of problems is concerned with specifying optimal schedules or combinations. ... The problem generalizes to one of finding a minimum cost path over the ...
Supposedly, a node having a low evaluation is more likely to be on an optimal path. The way in which GRAPHSEARCH uses an evaluation function to order nodes ...
That is, f(n) is an estimate of the cost of a minimal cost path ... Often we are interested in knowing the cost k (s,n) of an optimal path from a given ...
(This path is the lowest cost path from s to n found so far by the search algorithm. ... then algorithm A will find an optimal path to a goal.
Next we would like to show that if a path from s to a goal node exists, ... (Recall that g”(n) is the cost of the optimal path from s to n, and that g(n) is ...
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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