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 232
... given evidence E P ( E | H1 ) = the probability that we will observe evidence E given that hypothesis i is true P ( H ) = the a priori probability that hypothesis i is true in the absence of any specific evidence . These probabilities ...
... given evidence E P ( E | H1 ) = the probability that we will observe evidence E given that hypothesis i is true P ( H ) = the a priori probability that hypothesis i is true in the absence of any specific evidence . These probabilities ...
Página 236
... given two observations is the measure of belief given only one observation plus some increment for the second observation . This increment is computed by first taking the difference between 1 ( certainty ) and the belief given only the ...
... given two observations is the measure of belief given only one observation plus some increment for the second observation . This increment is computed by first taking the difference between 1 ( certainty ) and the belief given only the ...
Página 514
... given larger biases . In effect , they are given control over a larger portion of the input space . In this way , units that consistently lose are eventually given a chance to win and adjust their weights in the direction of a ...
... given larger biases . In effect , they are given control over a larger portion of the input space . In this way , units that consistently lose are eventually given a chance to win and adjust their weights in the direction of a ...
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