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 119
... appear that later turn out to be irrelevant . For the sake of efficiency , it may be desirable to store these things at a very high level and then to analyze in detail only those inputs that appear to be important . A third problem with ...
... appear that later turn out to be irrelevant . For the sake of efficiency , it may be desirable to store these things at a very high level and then to analyze in detail only those inputs that appear to be important . A third problem with ...
Página 176
... appear in the program . If a goal has more than one conjunctive part , prove the parts in the order in which they appear , propagating variable bindings as they are determined during unification . We can illustrate this strategy with a ...
... appear in the program . If a goal has more than one conjunctive part , prove the parts in the order in which they appear , propagating variable bindings as they are determined during unification . We can illustrate this strategy with a ...
Página 606
... ( Appears as Figure 9.4 ) . Davis , R. and D. Lenat , Knowledge Based Systems in Artificial Intelligence , pp . 16 , 21–25 , 270 , 284. Copyright © 1982 , McGraw - Hill . ( Appears as Figure 17.16 , and the trace of AM in Chapter 17 ) ...
... ( Appears as Figure 9.4 ) . Davis , R. and D. Lenat , Knowledge Based Systems in Artificial Intelligence , pp . 16 , 21–25 , 270 , 284. Copyright © 1982 , McGraw - Hill . ( Appears as Figure 17.16 , and the trace of AM in Chapter 17 ) ...
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