Artificial IntelligenceMcGraw-Hill, 1991 - 621 páginas |
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Página 198
... nonmonotonic inference . ' Allowing such reasoning has a significant impact on a knowledge base . Non- monotonic ... nonmonotonic reasoning does not share this property , it is also called defeasible : a nonmonotonic inference may be ...
... nonmonotonic inference . ' Allowing such reasoning has a significant impact on a knowledge base . Non- monotonic ... nonmonotonic reasoning does not share this property , it is also called defeasible : a nonmonotonic inference may be ...
Página 200
... nonmonotonic reasoning capabilities to A , we get a new set of wff's , which we've labeled B.2 B ( usually ) ... nonmonotonic reasoning . 7.2.1 Default Reasoning We want to use nonmonotonic reasoning to perform what is commonly called ...
... nonmonotonic reasoning capabilities to A , we get a new set of wff's , which we've labeled B.2 B ( usually ) ... nonmonotonic reasoning . 7.2.1 Default Reasoning We want to use nonmonotonic reasoning to perform what is commonly called ...
Página 201
... nonmonotonic reasoning that can be defined in those logics : • Abduction • Inheritance Nonmonotonic Logic One system that provides a basis for default reasoning is Nonmonotonic Logic ( NML ) [ McDermott and Doyle , 1980 ] , in which the ...
... nonmonotonic reasoning that can be defined in those logics : • Abduction • Inheritance Nonmonotonic Logic One system that provides a basis for default reasoning is Nonmonotonic Logic ( NML ) [ McDermott and Doyle , 1980 ] , in which the ...
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