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 84
... update f ' of B to 6 ( since that is the best we think we can do , which we can achieve by going through G ) . This requires updating the cost of the AND arc B - C to 12 ( 6 + 4 + 2 ) . After doing that , the arc to D is again the ...
... update f ' of B to 6 ( since that is the best we think we can do , which we can achieve by going through G ) . This requires updating the cost of the AND arc B - C to 12 ( 6 + 4 + 2 ) . After doing that , the arc to D is again the ...
Página 506
... update rule requires that the activation function be continuous and differentiable . The derivation of the weight update rule is more complex than the derivation of the fixed - increment update rule for perceptrons , but the idea is ...
... update rule requires that the activation function be continuous and differentiable . The derivation of the weight update rule is more complex than the derivation of the fixed - increment update rule for perceptrons , but the idea is ...
Página 518
... update the state units . The network never settles into a stable state ; instead it changes at each time step . Recurrent networks can be trained with the backpropagation algorithm . At each step , we compare the activations of the ...
... update the state units . The network never settles into a stable state ; instead it changes at each time step . Recurrent networks can be trained with the backpropagation algorithm . At each step , we compare the activations of the ...
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