
Ask HN: What's happened to fuzzy logic? - probably
I've come across fuzzy logic mostly in pedagogical context these days. Of course there are still researchers and developers using fuzzy logic to develop algorithms, but has the mainstream future of AI been supplanted by  machine learning, natural language processing, computer vision, robotics, etc.? Or fuzzy logic has become a part of each or some of these subdisciplines of AI?
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_delirium
It's used somewhat in AI still, especially the communities going under the
name "computational intelligence" (CI). In many communities it's been
supplanted by various statistical approaches, though. Fuzzy logic starts from
logic, and adds in non-binary distinctions. An alternate approach is to start
from statistics, and add in structure: things like Markov logic networks,
Bayesian graphical models, probabilistic planning, hierarchical reinforcement
learning, and a dozen other such approaches. Both sets of approaches have the
same high-level goal of bringing together symbolic reasoning with numerical
inference, in some form.

The second basket of approaches (statistics+structure) is now more popular
overall than fuzzy logic I think, though it depends on what communities you're
from; fuzzy logic is much more popular in engineering-flavored disciplines
than in math-flavored disciplines.

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probably
Great summary of the situation -- did not expect. Thanks!

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mturmon
One technical problem with fuzzy logic, that is not suffered by statistics, is
that there's no universally-accepted way of combining fuzzy logic truth
values.

The rules for manipulation of conditional probabilities, which statistics
inherited from probability theory, allow building up complicated statistical
models (i.e., complex enough to capture real-world applications) from pieces.
This is what the comment by _delirium is saying.

There's no such calculus for the truth values in fuzzy logic. The core problem
is, what the concept of "truth value" refers to. In conventional Statistics,
probabilities can be grounded in relative frequencies, and in principle
measured in real experiments. The same can't be said of a fuzzy truth value.

That much said, there are some people working on generalizations of
probability theory to situations where relative frequencies don't make sense,
and there's an overlap between the more sophisticated of the fuzzy logic
theorists and this community. See, for example,
<http://www.sipta.org/isipta11/>, or
<http://en.wikipedia.org/wiki/Imprecise_probability>

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probably
Thanks -- didn't realize this thread was still alive. So the showstopper is
that there is no equivalent to Bayes theorem for fuzzy logic, you're saying.
Great clarification.

