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.
Results 1-5 of 6
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
9 GO LOOP This procedure is sufficiently general to encompass a wide variety of
special graph-searching algorithms. The procedure generates an explicit graph,
G, called the search graph and a subset, T, of G called the search tree.
In this case, each member of M is added to OPEN and is installed in the search
tree as a successor of n. The search graph is the search tree throughout the
execution of the algorithm, and there is no need to change parents of the nodes
in T. If ...
graph and search tree shown in Figure 2.4. The dark arrows along certain arcs in
this search graph are the pointers that define parents of nodes in the search tree.
The solid nodes are on CLOSED, and the other nodes are on OPEN at the time ...
You have reached your viewing limit for this book.
What people are saying - Write a review
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