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 57
... tree structure in which each node represents a problem state and each arc represents a relationship between the states represented by the nodes it connects . For example , Figure 2.18 shows part of a search tree for a water jug problem ...
... tree structure in which each node represents a problem state and each arc represents a relationship between the states represented by the nodes it connects . For example , Figure 2.18 shows part of a search tree for a water jug problem ...
Página 58
... tree . For example , in the tree shown in Figure 2.18 , the node ( 4 , 3 ) , representing 4 gallons of water in one jug and 3 gallons in the other , can be generated either by first filling the 4 - gallon jug and then the 3 - gallon one ...
... tree . For example , in the tree shown in Figure 2.18 , the node ( 4 , 3 ) , representing 4 gallons of water in one jug and 3 gallons in the other , can be generated either by first filling the 4 - gallon jug and then the 3 - gallon one ...
Página 470
... trees , as exemplified by the ID3 program of Quinlan [ 1986 ] . ID3 uses a tree representation for concepts , such as the one shown in Figure 17.13 . To classify a particular input , we start at the top of the tree and answer questions ...
... trees , as exemplified by the ID3 program of Quinlan [ 1986 ] . ID3 uses a tree representation for concepts , such as the one shown in Figure 17.13 . To classify a particular input , we start at the top of the tree and answer questions ...
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