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 98
... potential solutions ( as in hill climbing ) . Sometimes they represent solutions that are partially specified ( as in constraint satisfaction ) . • How , at each stage of the search process , a state is selected for expansion . • How ...
... potential solutions ( as in hill climbing ) . Sometimes they represent solutions that are partially specified ( as in constraint satisfaction ) . • How , at each stage of the search process , a state is selected for expansion . • How ...
Página 302
... potential designs of such devices requires the same capability . Of course , as soon as we begin to think about modeling such complex entities , it becomes clear that the best we will be able to do is create an approximate model . There ...
... potential designs of such devices requires the same capability . Of course , as soon as we begin to think about modeling such complex entities , it becomes clear that the best we will be able to do is create an approximate model . There ...
Página 618
... potential fields 571 pragmatics 380 , 384 , 415 pre - image 573 preconditions 95 predicate logic 113 , 131 , 133 , 227 , 291 , 297 , 299 , 302 , 304 , 338 , 529 preference semantics 399 primitives , semantic 118 , 277 , 282 , 284 ...
... potential fields 571 pragmatics 380 , 384 , 415 pre - image 573 preconditions 95 predicate logic 113 , 131 , 133 , 227 , 291 , 297 , 299 , 302 , 304 , 338 , 529 preference semantics 399 primitives , semantic 118 , 277 , 282 , 284 ...
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