Principles of Artificial Intelligence
Morgan Kaufmann, Jun 28, 2014 - Computers - 476 pages
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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8 :: 8T8T d4T||: l| | : : ::| :: ll| .| : : | : ::| :: :: 45 | () & Goal Node 19 :| :: ii : : i ::: |:: i|. Fig. 2.6A search tree produced by a depth-first search. Fig. 2.8A search tree using an evaluation function.
One important method uses a real-valued function over the nodes called an evaluation function. Evaluation functions have been based on a variety of ideas: Attempts have been made to define the probability that a node is on the best path ...
We see that the same solution path is found here as was found by the other search methods, although the use of the evaluation function has resulted in substantially fewer nodes being expanded. (If we simply use the evaluation function ...
Before demonstrating some of the properties of this evaluation function, we first introduce some helpful notation. Let the function k (ni, n,) give the actual cost of a minimal cost path between two arbitrary nodes n, and n, .
Such information might be similar to that used in the function W(n) in the 8-puzzle example. We call h the heuristic function and will discuss it in more detail later. Suppose we now use as an evaluation function f(n) = g(n) + h(n).
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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