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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... version space algorithm , we should make some observa- tions about the representation . Some descriptions are more general than others . For example , the description in Figure 17.9 is more general than the one in Figure 17.7 . In fact ...
... version space algorithm , we should make some observa- tions about the representation . Some descriptions are more general than others . For example , the description in Figure 17.9 is more general than the one in Figure 17.7 . In fact ...
Página 467
... version space that do not cover the example . That is , specialize the elements of G as little as possible so that the negative example is no longer covered by any of the elements of G. 4. If S and G are both singleton sets , then if ...
... version space that do not cover the example . That is , specialize the elements of G as little as possible so that the negative example is no longer covered by any of the elements of G. 4. If S and G are both singleton sets , then if ...
Página 468
... version space ( a space of candidate descrip- tions ) that can be translated roughly into English as : " The target concept may be as specific as ' Japanese , blue economy car , ' or as general as either ' blue car ' or ' economy car ...
... version space ( a space of candidate descrip- tions ) that can be translated roughly into English as : " The target concept may be as specific as ' Japanese , blue economy car , ' or as general as either ' blue car ' or ' economy car ...
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