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 16
... capture the meaning of the sentences . It also converts questions into that form . It finds answers by matching structured forms against each other . Data Structures EnglishKnow InputText Structured Text A description of the words ...
... capture the meaning of the sentences . It also converts questions into that form . It finds answers by matching structured forms against each other . Data Structures EnglishKnow InputText Structured Text A description of the words ...
Página 247
... capturing different kinds of information . As an example , consider the proposition John was pretty sure that Mary was seriously ill . Bayesian approaches naturally capture John's degree of certainty , while fuzzy techniques can ...
... capturing different kinds of information . As an example , consider the proposition John was pretty sure that Mary was seriously ill . Bayesian approaches naturally capture John's degree of certainty , while fuzzy techniques can ...
Página 294
... capture the differences in meaning between the two sentences of each pair . John slapped Bill . John punched Bill . Bill drank his Coke . Bill slurped his Coke . Sue likes Dickens . Sue adores Dickens . 3. Construct a script for going ...
... capture the differences in meaning between the two sentences of each pair . John slapped Bill . John punched Bill . Bill drank his Coke . Bill slurped his Coke . Sue likes Dickens . Sue adores Dickens . 3. Construct a script for going ...
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