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
... literal node , n , of the graph , we add a new descendant of node n , labeled by the matching goal literal , to the graph . This descendant is called a goal node . Goal nodes ... nodes . ( At termination , the system has essentially inferred ...
... literal node , n , of the graph , we add a new descendant of node n , labeled by the matching goal literal , to the graph . This descendant is called a goal node . Goal nodes ... nodes . ( At termination , the system has essentially inferred ...
Page 206
... literal , W is a wff in AND / OR form , and all expressions might contain ... nodes and that include the newly added match arc . The clauses corresponding ... nodes of the solution graphs . These clauses are just those that could be ...
... literal , W is a wff in AND / OR form , and all expressions might contain ... nodes and that include the newly added match arc . The clauses corresponding ... nodes of the solution graphs . These clauses are just those that could be ...
Page 210
... literal nodes labeled by R ( A ) and Q ( x ) . If the same rule is applied more than once , it is important that ... literal , L , unifies with a literal L ' labeling a literal node , n , of the graph , we can add a match arc ( labeled ...
... literal nodes labeled by R ( A ) and Q ( x ) . If the same rule is applied more than once , it is important that ... literal , L , unifies with a literal L ' labeling a literal node , n , of the graph , we can add a match arc ( labeled ...
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 contains control regime control strategy cost DCOMP 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(B implication initial state description knowledge leaf nodes literal nodes logic negation node labeled ONTABLE(A optimal path pickup(A precondition predicate calculus problem-solving procedure production system proof prove recursive regress represent representation result robot problem rule applications search graph search tree selected semantic network sequence shown in Figure Skolem function solution graph solve SRI International stack(A STRIPS structure subgoal substitutions successors Suppose symbols termination condition theorem theorem-proving tip nodes unifying composition universally quantified unstack(C,A variables WORKS-IN