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 147
... exploit the associative property of or [ i.e. , a V ( bv c ) = ( a V b ) V c ] and simply remove the parentheses , giving ¬Roman ( x ) V ¬know ( x , Marcus ) \ hate ( x , Caesar ) V - hate ( y , z ) V thinkcrazy ( x , y ) However , it ...
... exploit the associative property of or [ i.e. , a V ( bv c ) = ( a V b ) V c ] and simply remove the parentheses , giving ¬Roman ( x ) V ¬know ( x , Marcus ) \ hate ( x , Caesar ) V - hate ( y , z ) V thinkcrazy ( x , y ) However , it ...
Página 341
... exploit some heuristic knowledge . HOLDING ( x ) is very easy to achieve . At most , it is necessary to put down something else and then to pick up the desired object . But HOLDING is also very easy to undo . In order to do almost ...
... exploit some heuristic knowledge . HOLDING ( x ) is very easy to achieve . At most , it is necessary to put down something else and then to pick up the desired object . But HOLDING is also very easy to undo . In order to do almost ...
Página 549
... exploit a justification - based truth maintenance system to revise its model of the circuit . The first rule shown here says that an element should be criticized for poor resetability if its sequential level count is greater than two ...
... exploit a justification - based truth maintenance system to revise its model of the circuit . The first rule shown here says that an element should be criticized for poor resetability if its sequential level count is greater than two ...
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