Artificial IntelligenceMcGraw-Hill, 1991 - 621 páginas |
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Página 368
... drawing shown in Figure 14.9 . Assume either that you have been given this drawing as the input or that lower - level routines have already operated to extract these lines from an input photograph . The next step in the analysis process ...
... drawing shown in Figure 14.9 . Assume either that you have been given this drawing as the input or that lower - level routines have already operated to extract these lines from an input photograph . The next step in the analysis process ...
Página 447
... drawn from the same population more efficiently and more effectively the next time . As thus defined , learning covers ... draw heavily on knowledge as their source of power . Knowledge is generally acquired through experience , and such ...
... drawn from the same population more efficiently and more effectively the next time . As thus defined , learning covers ... draw heavily on knowledge as their source of power . Knowledge is generally acquired through experience , and such ...
Página 499
... drawing stages . We could draw one line to isolate the point ( x1 = 1 , x2 = 1 ) and then another line to divide the remaining three points into two categories . Using this idea , we can construct a " multilayer " perceptron ( a series ...
... drawing stages . We could draw one line to isolate the point ( x1 = 1 , x2 = 1 ) and then another line to divide the remaining three points into two categories . Using this idea , we can construct a " multilayer " perceptron ( a series ...
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