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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... optimal schedules or combinations. Many of these problems can be attacked by the methods discussed in this book. A ... path over the edges of a graph containing n nodes such that the path visits each of the n nodes precisely once. Many ...
... path; distance or difference metrics between an arbitrary node and the goal set have been suggested; or in board ... optimal path. The way in which GRAPHSEARCH uses an evaluation function to order nodes can be illustrated by considering ...
... path constrained to go through node n. That node on OPEN having the smallest value off is then the node estimated to ... optimal path from n to a goal. (The function h” is undefined for any node n that has no accessible goal node.) Often ...
... path is the lowest cost path from s to n found so far by the search algorithm. The value of g(n) for certain nodes ... optimal path to a goal. When algorithm A uses an h function that is a lower bound on h", we call it algorithm A* (read ...
... shortest path in the implicit graph being searched from s to any node n in the search tree produced by A*. Then since the cost of each arc in the graph is at least some small positive numbere, g”(n) > d”(n)e. (Recall that g”(n) is the ...
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