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 243
... elements of O. However , not all evidence is directly supportive of individual elements . Often it supports sets of elements ( i.e. , subsets of ) . For example , in our diagnosis problem , fever might support { Flu , Cold , Pneu } . In ...
... elements of O. However , not all evidence is directly supportive of individual elements . Often it supports sets of elements ( i.e. , subsets of ) . For example , in our diagnosis problem , fever might support { Flu , Cold , Pneu } . In ...
Página 260
... elements and an instance ( instance ) of a class of sets , from which it inherits its set - level properties . To make this distinction clear , it is useful to distinguish between regular classes , whose elements are individual entities ...
... elements and an instance ( instance ) of a class of sets , from which it inherits its set - level properties . To make this distinction clear , it is useful to distinguish between regular classes , whose elements are individual entities ...
Página 265
... elements of its range ( its possible values ) . A relation is a set of ordered pairs . Thus it makes sense to say ... elements of the range must be elements ; range - constraint contains a logical expression that further constrains the ...
... elements of its range ( its possible values ) . A relation is a set of ordered pairs . Thus it makes sense to say ... elements of the range must be elements ; range - constraint contains a logical expression that further constrains the ...
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