Principles of Artificial IntelligenceA 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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Page 204
Goal Nodes C G C D E G Rules : A CAD B = EAG А B А B Fact ( A V B ) Fig . 6.4
An AND / OR graph satisfying termination . simple goal expressions . ) Goal
literals ( as well as rules ) can be used to add descendants to the AND / OR
graph .
Goal Nodes C G C D E G Rules : A CAD B = EAG А B А B Fact ( A V B ) Fig . 6.4
An AND / OR graph satisfying termination . simple goal expressions . ) Goal
literals ( as well as rules ) can be used to add descendants to the AND / OR
graph .
Page 213
We can also imagine mixed systems in which three different forms of resolution
are used , namely , resolution between assertions , resolution between goal
expressions , and resolution between an assertion and a goal . The forward
system ...
We can also imagine mixed systems in which three different forms of resolution
are used , namely , resolution between assertions , resolution between goal
expressions , and resolution between an assertion and a goal . The forward
system ...
Page 264
We first encounter GOAL ( BOSSOF ? u ? v ) . Since no facts match this goal , we
look for B - rules and find BR1 . The pattern match merely passes along the
existential variables . The computational environment is now as shown in Figure
6.32 ...
We first encounter GOAL ( BOSSOF ? u ? v ) . Since no facts match this goal , we
look for B - rules and find BR1 . The pattern match merely passes along the
existential variables . The computational environment is now as shown in Figure
6.32 ...
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Common terms and phrases
achieve actions algorithm AND/OR graph answer applied arcs Artificial Intelligence assume attempt backtracking backward block called chapter clause CLEAR(C complete component condition consider consistent contains control strategy corresponding cost database deduction Deleters described direction discussed evaluation example expression F-rule fact Figure formula function given global database goal goal node goal stack goal wff HANDEMPTY heuristic important initial involves JOHN knowledge labeled language literals logic match methods move namely node Note obtained occur ONTABLE(A operation path possible precondition predicate calculus problem procedure production system proof prove quantified reasoning refutation represent representation resolution result robot rule satisfied selected sequence shown in Figure simple solution graph solve specify statement step STRIPS structure subgoal substitutions successors Suppose symbols termination theorem unifying unit University variables
Bibliographic information
| Title | Principles of Artificial Intelligence Symbolic computation |
| Author | Nils J. Nilsson |
| Edition | illustrated |
| Publisher | Tioga Publishing Company, 1980 |
| Original from | the University of Michigan |
| Digitized | 15 Nov 2007 |
| ISBN | 0935382011, 9780935382013 |
| Length | 476 pages |
| Export Citation | BiBTeX EndNote RefMan |