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Fuzzy Grey Cognitive Maps
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Decision support systems are extremely useful in the medical domain.
The creation of such systems requires precise medical parameters which
are not always available. One possible solution is to approximate such
parameters, knowing they won’t be completely correct. However, this
error can have a big impact on the result. Recently prof. J. L. Salmeron
has proposed a new technology named fuzzy grey cognitive maps. This
technology enables someone to deal with a lot of uncertainty. Therefore
fuzzy grey cognitive maps seem like a perfect fit to use for medical
purposes.

Fuzzy cognitive maps use directed graphs to represent knowledge and the
fuzzy set theory to give variables a membership between zero and one
instead of only the values zero and one. On top of that it allows us to
denote the relation between two variables with a correlation coefficient.
Decisions are made by using an iterative algorithm.

An extension of fuzzy cognitive maps, named fuzzy grey cognitive maps,
introduces the use of intervals as the value for both the relation
between two variables and the variable itself. This severely improves
the expressivity and the possibility to handle uncertain variables and
relations. We implement the algorithm using N3 and the EYE reasoner.
The fact that relations do not have to be given a precise value is a
tremendous benefit.

-- Gijs Muys

The tests are at http://josd.github.io/eye/reasoning/fgcm/test
