Artificial Intelligence |
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Página 232
If both are present, we need to take both into account in determining the total
weight of evidence. But, since spots and fever are not independent events, we
cannot just sum their effects. Instead, we need to represent explicitly the
conditional ...
If both are present, we need to take both into account in determining the total
weight of evidence. But, since spots and fever are not independent events, we
cannot just sum their effects. Instead, we need to represent explicitly the
conditional ...
Página 232
If we know the prior probabilities of finding each of the various minerals and we
know the probabilities that if a mineral is present then certain physical
characteristics will be observed , then we can use Bayes ' formula to compute ,
from the ...
If we know the prior probabilities of finding each of the various minerals and we
know the probabilities that if a mineral is present then certain physical
characteristics will be observed , then we can use Bayes ' formula to compute ,
from the ...
Página 397
( b ) For each attribute A that is present ( at the top level ) in either G1 or G2 do i .
If A is not present at the top level in the other input , then add A and its value to
NEW . ii . If it is , then call Graph - Unify with the two values for A . If that fails , then
...
( b ) For each attribute A that is present ( at the top level ) in either G1 or G2 do i .
If A is not present at the top level in the other input , then add A and its value to
NEW . ii . If it is , then call Graph - Unify with the two values for A . If that fails , then
...
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Contenido
What Is Artificial Intelligence? | 3 |
Problems Problem Spaces and Search | 29 |
Heuristic Search Techniques | 63 |
Derechos de autor | |
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Términos y frases comunes
active addition agents algorithm allow answer apply approach assertions becomes belief build called Chapter clauses combined complete concept consider consistent constraints contains corresponding define dependency described discussed domain elements example fact Figure function given goal heuristic important initial input instance interpretation John kinds knowledge knowledge base labeled language learning logic look match meaning methods move natural necessary node object occur operators output particular path perform position possible predicate present problem procedure produce properties question reasoning relation represent representation result robot rules semantic sentence shown in Figure shows simple single situation slot solution solve space specific statements step stored structure Suppose task techniques things tree true understanding units usually variables weights