Artificial IntelligenceMcGraw-Hill, 1991 - 621 páginas |
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Página 39
... Depth - First Search Tree Algorithm : Depth - First Search 1. If the initial state is a goal state , quit and return success . 2. Otherwise , do the following until success or failure is signaled : ( a ) Generate a successor , E , of ...
... Depth - First Search Tree Algorithm : Depth - First Search 1. If the initial state is a goal state , quit and return success . 2. Otherwise , do the following until success or failure is signaled : ( a ) Generate a successor , E , of ...
Página 73
... first search and depth - first search ( of several varieties ) . In this section , we discuss a new method , best - first search , which is a way of combining the advantages of both depth - first and breadth - first search into a single ...
... first search and depth - first search ( of several varieties ) . In this section , we discuss a new method , best - first search , which is a way of combining the advantages of both depth - first and breadth - first search into a single ...
Página 323
... depth tree search will take ( because of variations in pruning efficiency ... first in the next iteration . With effective ordering , the alpha - beta ... depth - first search and breadth - first search . Depth - first search was ...
... depth tree search will take ( because of variations in pruning efficiency ... first in the next iteration . With effective ordering , the alpha - beta ... depth - first search and breadth - first search . Depth - first search was ...
Contenido
5 | 24 |
Heuristic Search Techniques | 63 |
Knowledge Representation Issues | 105 |
Derechos de autor | |
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Términos y frases comunes
Abbott agents algorithm answer apply approach ARMEMPTY assertions attributes axioms backpropagation backtracking backward belief best-first search breadth-first search Caesar called Chapter chess clauses complete concept conceptual dependency consider constraints contains contradiction corresponding define depth-first depth-first search described discussed domain fact frame function game tree goal grammar graph heuristic Horn clauses important inference inheritance input instance interpretation isa links John justification knowledge base knowledge representation labeled learning Marcus match minimax move MYCIN natural language node object ON(B operators output parsing particular path perceptron perform players possible preconditions predicate logic problem problem-solving procedure produce PROLOG properties represent result robot rules script Section semantic semantic net sentence shown in Figure simple slot solution solve specific step structure Suppose syntactic task techniques theorem things tree truth maintenance system understanding variables version space