## 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. |

### From inside the book

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**achieving**such focusing in chapter 2 . Even though we use graphs of this sort only with graph - search control ...**achieved**, it is possible that a complete reformulation of the problem ( changing the very notion of what a state is ... Page 34

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**achieve**this effect with production systems also . To do so , we must incorporate both state descriptions and goal descriptions into the global database . F - rules are applied to the state description part , while B - rules are applied ... Page 35

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**achieved**regardless of the sequence of rules applied in the set { R1 , R2 , R3 } . We say that a production system is commutative if it has the following properties with respect to any database D : ( a ) Each member of the set of rules ... Page 61

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**achieve**this sort of flexibility , a control system must keep an explicit record of a graph of databases linked by rule applications . We say that control systems that operate in this manner use graph - search strategies . In our ... Page 62

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**achieved**, by explicitly storing just the initial database and records of incremental changes from which any of the other databases can rapidly be computed . 2.2.1 . GRAPH NOTATION We can think of a graph - search control strategy as a ...### 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