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 313
... SCORE to the minimum value that STATIC can return . It will be updated to reflect the best score that can be achieved by an element of SUCCES- SORS . For each element SUCC of SUCCESSORS , do the following : ( a ) Set RESULT - SUCC to ...
... SCORE to the minimum value that STATIC can return . It will be updated to reflect the best score that can be achieved by an element of SUCCES- SORS . For each element SUCC of SUCCESSORS , do the following : ( a ) Set RESULT - SUCC to ...
Página 314
... SCORE PATH = BEST - PATH When the initial call to MINIMAX returns , the best move from CURRENT is the first element ... score of -5 or less at C ( since the opponent is the minimizing player ) . But we also know that we are guaranteed a ...
... SCORE PATH = BEST - PATH When the initial call to MINIMAX returns , the best move from CURRENT is the first element ... score of -5 or less at C ( since the opponent is the minimizing player ) . But we also know that we are guaranteed a ...
Página 315
... score will be no better than 0 and a score of 3 can be achieved by moving to B instead . Now let's see how the value of beta can be used . After cutting off further exploration of I , J is examined , yielding a value of 5 , which is ...
... score will be no better than 0 and a score of 3 can be achieved by moving to B instead . Now let's see how the value of beta can be used . After cutting off further exploration of I , J is examined , yielding a value of 5 , which is ...
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