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 260
... properties that its elements possess . We want to use inheritance to infer those properties from general knowledge about the set . But a class is also an entity in itself . It may possess properties that belong not to the individual ...
... properties that its elements possess . We want to use inheritance to infer those properties from general knowledge about the set . But a class is also an entity in itself . It may possess properties that belong not to the individual ...
Página 263
... properties . Some of the properties we would like to be able to represent and use in reasoning include : • The classes to which the attribute can be attached , i.e. , for what classes does it make sense ? For example , weight makes ...
... properties . Some of the properties we would like to be able to represent and use in reasoning include : • The classes to which the attribute can be attached , i.e. , for what classes does it make sense ? For example , weight makes ...
Página 293
... properties include things like number - of - legs . Objects tend to inherit their extrinsic properties from IndividualObjects . • Event and Process - An Event is anything with temporal extent , e.g. , Walking . Process is a subclass of ...
... properties include things like number - of - legs . Objects tend to inherit their extrinsic properties from IndividualObjects . • Event and Process - An Event is anything with temporal extent , e.g. , Walking . Process is a subclass of ...
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