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 75
... path constrained to go through node n . That node on OPEN having the smallest value of ƒ is then the node estimated ... optimal path from n to a goal . ( The function h * is undefined for any node n that has no accessible goal node ...
... path constrained to go through node n . That node on OPEN having the smallest value of ƒ is then the node estimated ... optimal path from n to a goal . ( The function h * is undefined for any node n that has no accessible goal node ...
Page 77
... shortest path in the implicit graph being searched from s to any node n in the search tree produced by A * . Then since the cost of each arc in the graph is at least some small positive number e , g * ( n ) ≥ d * ( n ) e . ( Recall ...
... shortest path in the implicit graph being searched from s to any node n in the search tree produced by A * . Then since the cost of each arc in the graph is at least some small positive number e , g * ( n ) ≥ d * ( n ) e . ( Recall ...
Page 78
... path is equal to f * ( s ) , the minimal cost , and therefore ƒ ( n ' ) ≤ f * ( s ) . Thus , we have : RESULT 2 : At any time before A * terminates , there exists on OPEN a node n ' that is on an optimal path from s to a goal node ...
... path is equal to f * ( s ) , the minimal cost , and therefore ƒ ( n ' ) ≤ f * ( s ) . Thus , we have : RESULT 2 : At any time before A * terminates , there exists on OPEN a node n ' that is on an optimal path from s to a goal node ...
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 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(A 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 universally quantified unstack(C,A variables WORKS-IN