| slovo | definícia |
ai-complete
(foldoc) | AI-complete
/A-I k*m-pleet'/ (MIT,
Stanford: by analogy with "NP-complete") A term used to
describe problems or subproblems in artificial intelligence,
to indicate that the solution presupposes a solution to the
"strong AI problem" (that is, the synthesis of a human-level
intelligence). A problem that is AI-complete is, in other
words, just too hard.
See also gedanken.
[Jargon File]
(1995-04-12)
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ai-complete
(jargon) | AI-complete
/A·I k@m·pleet'/, adj.
[MIT, Stanford: by analogy with NP-complete (see NP-)] Used to describe
problems or subproblems in AI, to indicate that the solution presupposes a
solution to the ‘strong AI problem’ (that is, the synthesis of a
human-level intelligence). A problem that is AI-complete is, in other
words, just too hard.
Examples of AI-complete problems are ‘The Vision Problem’ (building a
system that can see as well as a human) and ‘The Natural Language Problem’
(building a system that can understand and speak a natural language as well
as a human). These may appear to be modular, but all attempts so far (2003)
to solve them have foundered on the amount of context information and
‘intelligence’ they seem to require. See also gedanken.
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| | podobné slovo | definícia |
ai-complete
(foldoc) | AI-complete
/A-I k*m-pleet'/ (MIT,
Stanford: by analogy with "NP-complete") A term used to
describe problems or subproblems in artificial intelligence,
to indicate that the solution presupposes a solution to the
"strong AI problem" (that is, the synthesis of a human-level
intelligence). A problem that is AI-complete is, in other
words, just too hard.
See also gedanken.
[Jargon File]
(1995-04-12)
|
ai-complete
(jargon) | AI-complete
/A·I k@m·pleet'/, adj.
[MIT, Stanford: by analogy with NP-complete (see NP-)] Used to describe
problems or subproblems in AI, to indicate that the solution presupposes a
solution to the ‘strong AI problem’ (that is, the synthesis of a
human-level intelligence). A problem that is AI-complete is, in other
words, just too hard.
Examples of AI-complete problems are ‘The Vision Problem’ (building a
system that can see as well as a human) and ‘The Natural Language Problem’
(building a system that can understand and speak a natural language as well
as a human). These may appear to be modular, but all attempts so far (2003)
to solve them have foundered on the amount of context information and
‘intelligence’ they seem to require. See also gedanken.
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