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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... objects . For instance , consider one block perfectly aligned on top of another . The objects are EQUAL in the length and width dimensions , while they MEET in the height dimension . If the top block is smaller than the bottom one but ...
... objects . For instance , consider one block perfectly aligned on top of another . The objects are EQUAL in the length and width dimensions , while they MEET in the height dimension . If the top block is smaller than the bottom one but ...
Página 538
... objects , not in terms of the objects themselves . It is particularly useful to take this point of view with liquids , since liquid “ objects ” can be split and merged so easily . For example , if we consider a river to be a piece of ...
... objects , not in terms of the objects themselves . It is particularly useful to take this point of view with liquids , since liquid “ objects ” can be split and merged so easily . For example , if we consider a river to be a piece of ...
Página 564
... objects , and some objects may partially occlude others , as we saw earlier in Figure 14.8 . • The value of a single pixel is affected by many different phenomena , including the color of the object , the source of the light , the angle ...
... objects , and some objects may partially occlude others , as we saw earlier in Figure 14.8 . • The value of a single pixel is affected by many different phenomena , including the color of the object , the source of the light , the angle ...
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