Artificial IntelligenceMcGraw-Hill, 1991 - 621 páginas |
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Página 265
... domain ( the classes for which it makes sense ) to elements of its range ( its possible values ) . A relation is a ... domain and a range . We represent the domain in the slot labeled domain . We break up the representation of the range ...
... domain ( the classes for which it makes sense ) to elements of its range ( its possible values ) . A relation is a ... domain and a range . We represent the domain in the slot labeled domain . We break up the representation of the range ...
Página 270
... domain - independent slot attributes , slots may have domain- specific properties that support problem solving in a particular domain . Since these slots are not treated explicitly by the frame - system interpreter , they will be useful ...
... domain - independent slot attributes , slots may have domain- specific properties that support problem solving in a particular domain . Since these slots are not treated explicitly by the frame - system interpreter , they will be useful ...
Página 442
... domain reasoning , one of its important uses is to exploit one blackboard for reasoning in the problem domain and another for controlling that reasoning . In addition , these systems provide a goal - structured agenda mechanism that can ...
... domain reasoning , one of its important uses is to exploit one blackboard for reasoning in the problem domain and another for controlling that reasoning . In addition , these systems provide a goal - structured agenda mechanism that can ...
Contenido
5 | 24 |
Heuristic Search Techniques | 63 |
Knowledge Representation Issues | 105 |
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 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