Artificial Intelligence |
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Page 400
... semantic markers . For example , to interpret the sentence about Susan's diamond correctly , we must mark one sense of diamond as SHIMMERABLE , while the other two are marked NONSHIMMERABLE . As the number of such markers grows , the ...
... semantic markers . For example , to interpret the sentence about Susan's diamond correctly , we must mark one sense of diamond as SHIMMERABLE , while the other two are marked NONSHIMMERABLE . As the number of such markers grows , the ...
Page 403
... semantically and thus cannot be generated by a semantic grammar . Consider , for example , the sentence " I want to print stuff.txt on printer3 . " During a strictly syntactic parse , it would not be possible to decide whether the ...
... semantically and thus cannot be generated by a semantic grammar . Consider , for example , the sentence " I want to print stuff.txt on printer3 . " During a strictly syntactic parse , it would not be possible to decide whether the ...
Page 410
... semantic interpretation . Each time syntactic constituents are combined to form a larger syntactic unit , their corresponding semantic interpretations can be combined to form a larger semantic unit . The necessary rules for combining ...
... semantic interpretation . Each time syntactic constituents are combined to form a larger syntactic unit , their corresponding semantic interpretations can be combined to form a larger semantic unit . The necessary rules for combining ...
Contents
5 | 24 |
Heuristic Search Techniques | 63 |
Knowledge Representation Issues | 105 |
Copyright | |
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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