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 66
... better than the current state , then continue in the loop . The key difference between this algorithm and the one we ... better than another ? " For the algorithm to work , a precise definition of better must be provided . In some cases ...
... better than the current state , then continue in the loop . The key difference between this algorithm and the one we ... better than another ? " For the algorithm to work , a precise definition of better must be provided . In some cases ...
Página 67
... better than the current state is selected . The algorithm works as follows . Algorithm : Steepest - Ascent Hill Climbing 1. Evaluate the initial state . If it is also a goal state , then return it and quit . Otherwise , continue with ...
... better than the current state is selected . The algorithm works as follows . Algorithm : Steepest - Ascent Hill Climbing 1. Evaluate the initial state . If it is also a goal state , then return it and quit . Otherwise , continue with ...
Página 77
... better just as in step 2 ( c ) , and set the parent link and g and f ' values appropriately . If we have just found a better path to OLD , we must propagate the improvement to OLD's successors . This is a bit tricky . OLD points to its ...
... better just as in step 2 ( c ) , and set the parent link and g and f ' values appropriately . If we have just found a better path to OLD , we must propagate the improvement to OLD's successors . This is a bit tricky . OLD points to its ...
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