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 100
Note that node no has a l - connector directed to successor n , and a 2 -
connector directed to the set of successors { nm , ng . For k > 1 , k - connectors
are denoted in our illustrations by a curved line joining the arcs from parent to
elements of ...
Note that node no has a l - connector directed to successor n , and a 2 -
connector directed to the set of successors { nm , ng . For k > 1 , k - connectors
are denoted in our illustrations by a curved line joining the arcs from parent to
elements of ...
Page 169
Note that the first level of Figure 5 . 4 is the same as the first level of Figure 5 . 2 .
At subsequent levels , the linear - input form strategy does reduce the number of
clauses produced . Again , the use of this strategy on our example problem does
...
Note that the first level of Figure 5 . 4 is the same as the first level of Figure 5 . 2 .
At subsequent levels , the linear - input form strategy does reduce the number of
clauses produced . Again , the use of this strategy on our example problem does
...
Page 231
Examining the facts supporting this proof , we note some redundancy that could
have been avoided by use of the transitive property of G . That is , from C > E and
E > 0 , we ought to be able to derive C > 0 when needed , instead of having to ...
Examining the facts supporting this proof , we note some redundancy that could
have been avoided by use of the transitive property of G . That is , from C > E and
E > 0 , we ought to be able to derive C > 0 when needed , instead of having to ...
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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 efficient evaluation example expression F-rule fact Figure formula function given 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 |