Artificial IntelligenceMcGraw-Hill, 1991 - 621 páginas |
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Página 443
... build up its model of them . Using that model , it can choose the agents to whom it wishes to award bids . The chosen agents perform their tasks and then send reply messages to the manager . At another extreme , suppose we want to build ...
... build up its model of them . Using that model , it can choose the agents to whom it wishes to award bids . The chosen agents perform their tasks and then send reply messages to the manager . At another extreme , suppose we want to build ...
Página 581
... build a vision system to recognize the dead bulb One to build a vision system to locate a new bulb One to figure out how to grasp the lightbulb without breaking it One to figure out the arm solutions that will get the arm to the socket ...
... build a vision system to recognize the dead bulb One to build a vision system to locate a new bulb One to figure out how to grasp the lightbulb without breaking it One to figure out the arm solutions that will get the arm to the socket ...
Página 582
... build an English understanding program for the lightbulb - changing robot One to build a speech recognition system One to tell lightbulb jokes to the robot in between bulb - changing tasks One to build a language generation component so ...
... build an English understanding program for the lightbulb - changing robot One to build a speech recognition system One to tell lightbulb jokes to the robot in between bulb - changing tasks One to build a language generation component so ...
Contenido
5 | 24 |
Heuristic Search Techniques | 63 |
Knowledge Representation Issues | 105 |
Derechos de autor | |
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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