## 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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**solution graph**. Thus , the costs of the two**solution graphs**in Figure 3.2 are 8 and 7 if the cost of each k - connector is k . Beyond merely finding any**solution graph**from the start node 102 SEARCH STRATEGIES FOR DECOMPOSABLE PRODUCTION ...Page 106

... graph . ( Acyclicity of our graphs guarantees no loops in this upward propagation . ) The revised cost , q ( n ) , is an updated estimate of the cost of an optimal

... graph . ( Acyclicity of our graphs guarantees no loops in this upward propagation . ) The revised cost , q ( n ) , is an updated estimate of the cost of an optimal

**solution graph**from n to a set of terminal nodes . Only the ancestors of ...Page 237

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**solution graphs**thus joined constitutes a candidate solution if all of its other leaf nodes are terminal ( that is , if they are either goal nodes or if they participate in other RGR matches ) . Such a candidate**solution graph**is a final ...### 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