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 240
... subgoal P2 while adding PI to the set of facts that can be used in proving P2 or its subgoals . Then , if the system generates Pl as a subgoal of P2 , this subgoal can be matched against the assumed fact Pl . The process of ...
... subgoal P2 while adding PI to the set of facts that can be used in proving P2 or its subgoals . Then , if the system generates Pl as a subgoal of P2 , this subgoal can be matched against the assumed fact Pl . The process of ...
Page 288
... subgoal expressions . One strategy is to use B - rules that are based on the F - rules that we have just discussed . A B - rule that transforms a goal G into a subgoal G ' is logically based on the corresponding F - rule that when ...
... subgoal expressions . One strategy is to use B - rules that are based on the F - rules that we have just discussed . A B - rule that transforms a goal G into a subgoal G ' is logically based on the corresponding F - rule that when ...
Page 292
... subgoals greatly reduces the subgoal space . In Figure 7.5 we show the results of applying some B - rules to our example goal . ( The tail of each B - rule arc is adjacent to that goal literal used to match a literal in the add list of ...
... subgoals greatly reduces the subgoal space . In Figure 7.5 we show the results of applying some B - rules to our example goal . ( The tail of each B - rule arc is adjacent to that goal literal used to match a literal in the add list of ...
Contents
PROLOGUE | 1 |
PRODUCTION SYSTEMS AND AI | 17 |
SEARCH STRATEGIES FOR | 53 |
Copyright | |
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Common terms and phrases
8-puzzle achieve actions Adders AI production algorithm AND/OR graph applied Artificial Intelligence atomic formula backed-up value backtracking backward block breadth-first breadth-first search called chapter clause form CLEAR(C component CONT(Y,A contains control regime control strategy cost Deleters delineation depth-first search described discussed disjunction domain element-of evaluation function example existentially quantified F-rule formula frame problem global database goal expression goal node goal stack goal wff graph-search HANDEMPTY heuristic HOLDING(A implication initial state description knowledge literal nodes logic monotone restriction natural language processing negation node labeled ONTABLE(A optimal path pickup(A precondition predicate calculus problem-solving procedure production system proof prove recursive regress represent representation resolution refutation result robot problem rule applications search graph search tree selected semantic network sequence shown in Figure Skolem function solution graph solve stack(A STRIPS structure subgoal substitutions successors Suppose symbols termination condition theorem theorem-proving tip nodes universally quantified unstack(C,A variables WORKS-IN