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 390
... decisions about the function of each component as it is encountered , procrastinate the decision until enough information is available to make the decision correctly . Using this approach , when the word " have " of our example is ...
... decisions about the function of each component as it is encountered , procrastinate the decision until enough information is available to make the decision correctly . Using this approach , when the word " have " of our example is ...
Página 470
... Decision Tree training set , and the new concept then becomes one disjunct in the eventual disjunctive concept ... Decision Trees A third approach to concept learning is the induction of decision trees , as exemplified by the ID3 program ...
... Decision Tree training set , and the new concept then becomes one disjunct in the eventual disjunctive concept ... Decision Trees A third approach to concept learning is the induction of decision trees , as exemplified by the ID3 program ...
Página 471
... decision trees can be difficult for humans to understand , and so a decision tree system may have a hard time explaining the reasons for its classifications . 17.6 Explanation - Based Learning The previous section illustrated how we can ...
... decision trees can be difficult for humans to understand , and so a decision tree system may have a hard time explaining the reasons for its classifications . 17.6 Explanation - Based Learning The previous section illustrated how we can ...
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