Artificial IntelligenceMcGraw-Hill, 1991 - 621 páginas |
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Página 469
... concept from the version space prematurely . In the car example above , if the third training instance had been mislabeled ( - ) instead of ( + ) , the target concept of " Japanese economy car " would never be reached . Also , given ...
... concept from the version space prematurely . In the car example above , if the third training instance had been mislabeled ( - ) instead of ( + ) , the target concept of " Japanese economy car " would never be reached . Also , given ...
Página 472
... Concept - A high - level description of what the program is supposed to learn • An Operationality Criterion - A description of which concepts are usable • A Domain Theory - A set of rules that describe relationships between objects and ...
... Concept - A high - level description of what the program is supposed to learn • An Operationality Criterion - A description of which concepts are usable • A Domain Theory - A set of rules that describe relationships between objects and ...
Página 476
... concept X are also examples of another concept Y , create a new concept representing the intersection of X and Y. • If very few examples of a concept X are found , then add to the agenda the task of finding a generalization of X. Figure ...
... concept X are also examples of another concept Y , create a new concept representing the intersection of X and Y. • If very few examples of a concept X are found , then add to the agenda the task of finding a generalization of X. Figure ...
Contenido
5 | 24 |
Heuristic Search Techniques | 63 |
Knowledge Representation Issues | 105 |
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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 fact frame 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 operators output parsing particular path perceptron perform players possible preconditions predicate logic problem problem-solving procedure produce PROLOG properties 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