## 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

Results 1-5 of 74

Page 6

...

...

**solution**to a programming or robot control problem and then modify it ( to make it work correctly ) , than to insist ...**graph**containing n nodes such that the path visits each of the n nodes precisely once . Many puzzles have this ... Page 22

...

...

**solution**, the state of the computation reverts to the previous backtracking point , where another rule is applied instead , and the process continues . In the second type of tentative control regime , which we call**graph**- search ... Page 25

...

...

**solution**, the intervening steps are " forgotten , " and another rule is selected instead . Formally , the ...**Graph**Search .**Graphs**( or more specially , trees ) are extremely useful structures for keeping track of the effects of ... Page 27

...

...

**graph**- search control strategy grows such a tree until a database is produced that satisfies the termination ...**solution**requires more than an efficient control strategy . It requires selecting good representations for problem ... Page 31

...

...

**solution**must be of minimal distance . Figure 1.6 shows part of the search tree that might be generated by a**graph**- search control strategy in solving this problem . The numbers next to the edges of the tree are the increments of ...### 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 |

### Other editions - View all

### 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