## 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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**MONOTONE RESTRICTION**Describing the GRAPHSEARCH procedure , we noted that when a node n is expanded , some of its successors may already be on OPEN or CLOSED . The search tree may then need to be adjusted so that it defines the least ... Page 82

... restriction on h , when A * selects a node for expansion it has already found an optimal path to that node . Thus , with this restriction ...

... restriction on h , when A * selects a node for expansion it has already found an optimal path to that node . Thus , with this restriction ...

**monotone restriction**, we have that g * 82 SEARCH STRATEGIES FOR AI PRODUCTION SYSTEMS. Page 83

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**monotone restriction**is satisfied , then A * has already found an optimal path to any node it selects for expansion . That is , if A * selects n for expansion , and if the**monotone restriction**is satisfied , g ( n ) = g * ( n ) . The ... Page 84

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**monotone restriction**is satisfied , the f values of the sequence of nodes expanded by A * is nondecreasing . When the**monotone restriction**is not satisfied , it is possible that some node has a smaller f value at expansion than that of ...Page 95

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### Contents

1 | |

17 | |

53 | |

CHAPTER 3 SEARCH STRATEGIES FOR DECOMPOSABLE PRODUCTION SYSTEMS | 99 |

CHAPTER 4 THE PREDICATE CALCULUS IN AI | 131 |

CHAPTER 5 RESOLUTION REFUTATION SYSTEMS | 161 |

CHAPTER 6 RULEBASED DEDUCTION SYSTEMS | 193 |

CHAPTER 7 BASIC PLANGENERATING SYSTEMS | 275 |

CHAPTER 8 ADVANCED PLANGENERATING SYSTEMS | 321 |

CHAPTER 9 STRUCTURED OBJECT REPRESENTATIONS | 361 |

PROSPECTUS | 417 |

BIBLIOGRAPHY | 429 |

AUTHOR INDEX | 467 |

SUBJECT INDEX | 471 |

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### Common terms and phrases

8-puzzle achieve actions Adders 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 game tree 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 methods monotone restriction negation node labeled ONTABLE(A optimal path pickup(A precondition predicate calculus problem-solving procedure production rules production system proof prove recursive regress represent representation resolution refutation result robot problem rule applications search graph search tree 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 unifying composition universally quantified