## 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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... node has at most one parent . A node in the tree having no parent is called a root node . A node in the tree having ... goal set , and each node t in { t ; } is a

... node has at most one parent . A node in the tree having no parent is called a root node . A node in the tree having ... goal set , and each node t in { t ; } is a

**goal node**. A graph may be specified either explicitly or implicitly ... Page 64

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**goal node**. 2.2.2 . A GENERAL GRAPH - SEARCHING PROCEDURE * The process of explicitly generating part of an implicitly defined graph can be informally defined as follows . M already on CLOSED , decide for each of its. Procedure ... Page 65

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**goal node**, the process terminates successfully . The successful path from start node to**goal node**can then be recovered ( in reverse ) by tracing the pointers ... node ( except the root node ) of a tree 65 GRAPH - SEARCH STRATEGIES. Page 68

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**goal node**is generated by putting**goal nodes**at the very beginning of OPEN ; but , of course , this procedure would involve a goal test during step 8 of. Fig . 2.5 A search graph and search tree after expanding node 1 . Fig . 2.8 A ... Page 69

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**goal node**; they do not save the entire record of the search as do depth - first graph - search strategies . ) The search tree generated by a depth - first search process in an 8 - puzzle problem is illustrated in Figure 2.6 . The nodes ...### 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