Artificial IntelligenceMcGraw-Hill, 1991 - 621 páginas A revision of an established text for undergraduate and postgraduate Artificial Intelligence courses, this text incorporates the latest research and methods. |
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Página 309
... function is similar to that of the heuristic function h ' in the A * algorithm : in the absence of complete information , choose the most promising position . Of course , the static evaluation function could simply be applied directly ...
... function is similar to that of the heuristic function h ' in the A * algorithm : in the absence of complete information , choose the most promising position . Of course , the static evaluation function could simply be applied directly ...
Página 310
... evaluation function returns large values to indicate good situations for us , so our goal is to maximize the value of the static evaluation function of the next board position . An example of this operation is shown in Figure 12.1 . It ...
... evaluation function returns large values to indicate good situations for us , so our goal is to maximize the value of the static evaluation function of the next board position . An example of this operation is shown in Figure 12.1 . It ...
Página 453
... evaluation function produces values that are fairly reasonable measures of the correct score even if they are not as accurate as we hope to get them . Then this evaluation function can be used to provide feedback to itself . Move ...
... evaluation function produces values that are fairly reasonable measures of the correct score even if they are not as accurate as we hope to get them . Then this evaluation function can be used to provide feedback to itself . Move ...
Contenido
What Is Artificial Intelligence? | 3 |
5 | 24 |
Heuristic Search Techniques | 63 |
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 example fact 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 ON(C operators output parsing particular path perceptron perform players possible preconditions predicate logic problem problem-solving procedure produce PROLOG 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