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 23
... function . Our hill - climbing function must be such that it attains its highest value for a database satisfying the termination condition . Applying hill - climbing to the 8 - puzzle we might use , as a function of the state ...
... function . Our hill - climbing function must be such that it attains its highest value for a database satisfying the termination condition . Applying hill - climbing to the 8 - puzzle we might use , as a function of the state ...
Page 73
... function over the nodes called an 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 ...
... function over the nodes called an 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 ...
Page 74
... 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 determines ...
... 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 determines ...
Contents
PROLOGUE | 1 |
PRODUCTION SYSTEMS AND AI | 17 |
SEARCH STRATEGIES FOR | 53 |
Copyright | |
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Common terms and phrases
8-puzzle achieve actions Adders AI production 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 Deleters delineation depth-first search described discussed disjunction domain element-of evaluation function example existentially quantified F-rule formula frame problem global database goal expression goal node goal stack goal wff graph-search HANDEMPTY heuristic HOLDING(A implication initial state description knowledge literal nodes logic methods monotone restriction natural language processing negation node labeled ONTABLE(A optimal path pickup(A precondition predicate calculus problem-solving procedure production system proof prove recursive regress represent representation resolution refutation result robot problem rule applications search graph search tree selected 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 universally quantified unstack(C,A variables WORKS-IN