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 367
... Input Understanding is the process of interpreting an input and assigning it meaning . Unfortu- nately , in many understanding situations the input to which meaning should be assigned is not always the input that is presented to the ...
... Input Understanding is the process of interpreting an input and assigning it meaning . Unfortu- nately , in many understanding situations the input to which meaning should be assigned is not always the input that is presented to the ...
Página 504
... input units . Unlike the perceptron learning algorithm of the last section , the backpropagation algorithm usually updates its weights incrementally , after seeing each input - output pair . After it has seen all the input - output ...
... input units . Unlike the perceptron learning algorithm of the last section , the backpropagation algorithm usually updates its weights incrementally , after seeing each input - output pair . After it has seen all the input - output ...
Página 513
... input units directly connected to any number of output units . Produce : A set of weights such that the output units become active according to some natural division of the inputs . 1. Present an input vector , denoted ( x1 , x2 ...
... input units directly connected to any number of output units . Produce : A set of weights such that the output units become active according to some natural division of the inputs . 1. Present an input vector , denoted ( x1 , x2 ...
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