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 25
Page 68
... evaluation function. 68 SEARCH STRATEGIES FOR AI PRODUCTION SYSTEMS 2.3. Uninformed Graph-search Procedures.
... evaluation function. 68 SEARCH STRATEGIES FOR AI PRODUCTION SYSTEMS 2.3. Uninformed Graph-search Procedures.
Page 73
... evaluation function . Evaluation functions have been based on a variety of ideas : Attempts have been made to define the probability that a node is on the best path ; distance or difference metrics between an arbitrary node and the ...
... evaluation function . Evaluation functions have been based on a variety of ideas : Attempts have been made to define the probability that a node is on the best path ; distance or difference metrics between an arbitrary node and the ...
Page 74
... evaluation function has resulted in substantially fewer nodes being expanded . ( If we simply use the evaluation function f ( n ) = d ( n ) , we get the breadth - first search process . ) The choice of evaluation function critically ...
... evaluation function has resulted in substantially fewer nodes being expanded . ( If we simply use the evaluation function f ( n ) = d ( n ) , we get the breadth - first search process . ) The choice of evaluation function critically ...
Page 75
... evaluation function , we first introduce some helpful notation . Let the function k ( n1 , n , ) give the actual cost of a minimal cost path between two arbitrary nodes n , and n ,. ( The function k is undefined for nodes having no path ...
... evaluation function , we first introduce some helpful notation . Let the function k ( n1 , n , ) give the actual cost of a minimal cost path between two arbitrary nodes n , and n ,. ( The function k is undefined for nodes having no path ...
Page 76
... function and will discuss it in more detail later . Suppose we now use as an evaluation function f ( n ) = g ( n ) + h ( n ) . We call the GRAPHSEARCH algorithm using this evaluation function for ordering nodes , algorithm A. Note ...
... function and will discuss it in more detail later . Suppose we now use as an evaluation function f ( n ) = g ( n ) + h ( n ) . We call the GRAPHSEARCH algorithm using this evaluation function for ordering nodes , algorithm A. Note ...
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