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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... adjacent vertex and find all of its possible labelings . The line that we followed to get from the first vertex to the second must end up with only one label , and that label must be consistent with the two vertices it enters . So any ...
... adjacent vertex and find all of its possible labelings . The line that we followed to get from the first vertex to the second must end up with only one label , and that label must be consistent with the two vertices it enters . So any ...
Página 375
... adjacent to V and that has already been visited . Check to see that for each proposed labeling for V , there is a way to label the line between V and A in such a way that at least one of the labelings listed for A is still possible ...
... adjacent to V and that has already been visited . Check to see that for each proposed labeling for V , there is a way to label the line between V and A in such a way that at least one of the labelings listed for A is still possible ...
Página 376
... adjacent vertices in an attempt to constrain the set of labelings associated with V. And then we went back to each adjacent vertex A to see if the knowledge about V could be used to further constrain the labelings for A. Why could we ...
... adjacent vertices in an attempt to constrain the set of labelings associated with V. And then we went back to each adjacent vertex A to see if the knowledge about V could be used to further constrain the labelings for A. Why could we ...
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