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 Intelligence evolved 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 56
8 PATH_BACKTRACK ( RDATA ) ; BACKTRACK is called recursively on the new database . 9 if PATH = FAIL , go LOOP ; if the recursive call fails , try another rule . 10 return CONS ( R , PATH ) ; otherwise , pass the successful list of rules ...
8 PATH_BACKTRACK ( RDATA ) ; BACKTRACK is called recursively on the new database . 9 if PATH = FAIL , go LOOP ; if the recursive call fails , try another rule . 10 return CONS ( R , PATH ) ; otherwise , pass the successful list of rules ...
Page 60
Note that the recursive algorithm does not remember all databases that it visited previously . Backtracking involves “ forgetting ” all databases whose paths lead to failures . The algorithm remembers only those databases on the current ...
Note that the recursive algorithm does not remember all databases that it visited previously . Backtracking involves “ forgetting ” all databases whose paths lead to failures . The algorithm remembers only those databases on the current ...
Page 304
Once an F - rule was selected as relevant to removing a difference , GPS worked recursively on the preconditions for that F ... Thus , we see that recursive GPS is very similar to ( if slightly more general than ) recursive STRIPS .
Once an F - rule was selected as relevant to removing a difference , GPS worked recursively on the preconditions for that F ... Thus , we see that recursive GPS is very similar to ( if slightly more general than ) recursive STRIPS .
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Common terms and phrases
achieve actions algorithm AND/OR graph answer applied arcs assertions assume attempt backtracking backward block called chapter clause CLEAR(C complete component condition consider consistent contains control strategy corresponding cost database Deleters described direction discussed efficient evaluation example expanded 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 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 search tree selected sequence shown in Figure simple solution graph solve specify statement step STRIPS structure subgoal substitutions successors Suppose symbols termination unifying unit universal variables
Bibliographic information
| Title | Principles of Artificial Intelligence |
| Author | Nils J. Nilsson |
| Edition | illustrated, reprint |
| Publisher | Morgan Kaufmann Publishers, 1986 |
| Original from | the University of Virginia |
| Digitized | 27 Jul 2009 |
| ISBN | 0934613109, 9780934613101 |
| Length | 476 pages |
| Subjects | › Reference / General |
| Export Citation | BiBTeX EndNote RefMan |