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 228
... Consider again the problem of Dick , the Quaker and Republican , which we can rewrite using a slightly different kind of AB predicate as : Vx : Republican ( x ) ^ ¬AB1 ( x ) → ¬Pacifist ( x ) Vx : Quaker ( x ) ^ ¬¬AB2 ( x ) → Pacifist ...
... Consider again the problem of Dick , the Quaker and Republican , which we can rewrite using a slightly different kind of AB predicate as : Vx : Republican ( x ) ^ ¬AB1 ( x ) → ¬Pacifist ( x ) Vx : Quaker ( x ) ^ ¬¬AB2 ( x ) → Pacifist ...
Página 238
... consider what happens when independence assumptions are violated in the scenario of Figure 8.1 ( c ) . Let's consider a concrete example in which : S : sprinkler was on last night W : grass is wet R : it rained last night We can write ...
... consider what happens when independence assumptions are violated in the scenario of Figure 8.1 ( c ) . Let's consider a concrete example in which : S : sprinkler was on last night W : grass is wet R : it rained last night We can write ...
Página 415
... consider the discourse and pragmatic context in which the sentence was uttered ( as we saw in Section 15.1.1 ) ... Consider the text - Bill had a red balloon . John wanted it . The word " it " should be identified as referring to the red ...
... consider the discourse and pragmatic context in which the sentence was uttered ( as we saw in Section 15.1.1 ) ... Consider the text - Bill had a red balloon . John wanted it . The word " it " should be identified as referring to the red ...
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