Artificial IntelligenceMcGraw-Hill, 1991 - 621 páginas |
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Página 78
... optimal ( as determined by g ) path to a goal , if one exists . This can easily be seen from a few examples.5 Consider the situation shown in Figure 3.4 . Assume that the cost of all arcs is 1 . Initially , all nodes except A are on ...
... optimal ( as determined by g ) path to a goal , if one exists . This can easily be seen from a few examples.5 Consider the situation shown in Figure 3.4 . Assume that the cost of all arcs is 1 . Initially , all nodes except A are on ...
Página 79
... optimal solution . The formalization and proof of this corollary will be left as an exercise . The third observation ... optimal in that it generates the fewest nodes in the process of finding a solution to a problem . Under other ...
... optimal solution . The formalization and proof of this corollary will be left as an exercise . The third observation ... optimal in that it generates the fewest nodes in the process of finding a solution to a problem . Under other ...
Página 437
... optimal with respect to its goals . Unfortunately , in a complex world , an agent may not have enough processing power to behave optimally . This leads to a slightly weaker , but more useful , notion of bounded rationality [ Simon ...
... optimal with respect to its goals . Unfortunately , in a complex world , an agent may not have enough processing power to behave optimally . This leads to a slightly weaker , but more useful , notion of bounded rationality [ Simon ...
Contenido
5 | 24 |
Heuristic Search Techniques | 63 |
Knowledge Representation Issues | 105 |
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 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