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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Page 6
... optimal schedules or combinations . Many of these problems can be attacked by the methods discussed in this book . A ... path over the edges of a graph containing n nodes such that the path visits each of the n nodes precisely once ...
... optimal schedules or combinations . Many of these problems can be attacked by the methods discussed in this book . A ... path over the edges of a graph containing n nodes such that the path visits each of the n nodes precisely once ...
Page 73
... path ; distance or difference metrics between an arbitrary node and the goal set have been suggested ; or in board ... optimal path . The way in which GRAPHSEARCH uses an evaluation function to order nodes can be illustrated by ...
... path ; distance or difference metrics between an arbitrary node and the goal set have been suggested ; or in board ... optimal path . The way in which GRAPHSEARCH uses an evaluation function to order nodes can be illustrated by ...
Page 75
... path constrained to go through node n . That node on OPEN having the smallest value of f is then the node estimated ... optimal path from n to a goal . ( The function h * is undefined for any node n that has no accessible goal node ...
... path constrained to go through node n . That node on OPEN having the smallest value of f is then the node estimated ... optimal path from n to a goal . ( The function h * is undefined for any node n that has no accessible goal node ...
Page 76
... path is the lowest cost path from s to n found so far by the search algorithm . The value of g ( n ) for certain ... optimal path to a goal . When algorithm A uses an h function that is a lower bound on h * , we call it algorithm A ...
... path is the lowest cost path from s to n found so far by the search algorithm . The value of g ( n ) for certain ... optimal path to a goal . When algorithm A uses an h function that is a lower bound on h * , we call it algorithm A ...
Page 77
... path from s to a goal node exists , A * will terminate even for infinite graphs . To do so , let us suppose the ... optimal path from s to n , and that g ( n ) is the cost of the path in the search tree from s to node n . ) Clearly ...
... path from s to a goal node exists , A * will terminate even for infinite graphs . To do so , let us suppose the ... optimal path from s to n , and that g ( n ) is the cost of the path in the search tree from s to node n . ) Clearly ...
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