Artificial IntelligenceMcGraw-Hill, 1991 - 621 páginas |
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Página 277
... level of granularity , out of which representations of particular pieces of information can be constructed . I ← ATRANS◅ book where the symbols have the following 277 Strong Slot-and-Filler Structures Conceptual Dependency.
... level of granularity , out of which representations of particular pieces of information can be constructed . I ← ATRANS◅ book where the symbols have the following 277 Strong Slot-and-Filler Structures Conceptual Dependency.
Página 294
... conceptual dependency representation of the sentence John begged Mary for a pencil . How does this representation make it possible to answer the question Did John talk to Mary ? 2. One difficulty with representations that rely on a ...
... conceptual dependency representation of the sentence John begged Mary for a pencil . How does this representation make it possible to answer the question Did John talk to Mary ? 2. One difficulty with representations that rely on a ...
Página 406
... conceptual dependency ( CD ) structures . Parsing a sentence into a conceptual dependency representation is similar to the process of parsing using a case grammar . In both systems , the parsing process is heavily driven by a set of ...
... conceptual dependency ( CD ) structures . Parsing a sentence into a conceptual dependency representation is similar to the process of parsing using a case grammar . In both systems , the parsing process is heavily driven by a set of ...
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