Artificial IntelligenceMcGraw-Hill, 1991 - 621 páginas |
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Página 216
... labeled IN and every node in its OUT - list is labeled OUT . Notice that in Figure 7.5 , all of the assertions would have to be labeled OUT to be consistent . Alibi Abbott has no justification at all , much less a valid one , and so ...
... labeled IN and every node in its OUT - list is labeled OUT . Notice that in Figure 7.5 , all of the assertions would have to be labeled OUT to be consistent . Alibi Abbott has no justification at all , much less a valid one , and so ...
Página 218
... labeled OUT if the labeling is to be well - founded . The TMS task of ensuring a consistent , well - founded labeling has now been outlined . The other major task of a TMS is resolving contradictions . In a TMS , a contradiction node ...
... labeled OUT if the labeling is to be well - founded . The TMS task of ensuring a consistent , well - founded labeling has now been outlined . The other major task of a TMS is resolving contradictions . In a TMS , a contradiction node ...
Página 373
... labeled . New constraints will arise from this labeling and these constraints can be propagated back to vertices that have already been labeled , so the set of possible labelings for them is further reduced . This process proceeds until ...
... labeled . New constraints will arise from this labeling and these constraints can be propagated back to vertices that have already been labeled , so the set of possible labelings for them is further reduced . This process proceeds until ...
Contenido
5 | 24 |
Heuristic Search Techniques | 63 |
Knowledge Representation Issues | 105 |
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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 fact frame 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 operators output parsing particular path perceptron perform players possible preconditions predicate logic problem problem-solving procedure produce PROLOG properties 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