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 467
... example . If it is a negative example , first remove from S any descriptions that cover the example . Then , update the G set to contain the most general set of descriptions in the version space that do not cover the example . That is ...
... example . If it is a negative example , first remove from S any descriptions that cover the example . Then , update the G set to contain the most general set of descriptions in the version space that do not cover the example . That is ...
Página 468
... example . Now we come to the third example , a positive one . The first order of business is to remove from the G set any descriptions that are inconsistent with the positive example . Our new G set is : G = { ( x1 , x2 , Blue , X4 , X5 ) ...
... example . Now we come to the third example , a positive one . The first order of business is to remove from the G set any descriptions that are inconsistent with the positive example . Our new G set is : G = { ( x1 , x2 , Blue , X4 , X5 ) ...
Página 473
... example with respect to the goal concept . What is left is an explanation of why the training example is an instance of the goal concept . This explanation is expressed in terms that satisfy the operationality criterion . The next step ...
... example with respect to the goal concept . What is left is an explanation of why the training example is an instance of the goal concept . This explanation is expressed in terms that satisfy the operationality criterion . The next step ...
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