\comment{\subsection{Something about something on treatment learning}\label{sect:tars}
ITL utilizes the treatment learner {\it tar3}.
Tar3 is the third generation treatment learner
developed by Menzies {\it et. al.} and is 
explained in detail in \tion{tar3Details}.
There are several key characteristics of
tar3 that are worth re-iterating here.

First treatments are not classifiers, which
fits in nicely with ITL being a search technique, MORE.

Second, tar3 is a fast learner that can learn theories
from a small number of examples.  
The learning efficieny of the base heuristic
is important because our experience has been that
models are often very slow.
Reducing the number of times that model has to be
excuted therefore is very important in making
ITL usable.

Third, since the size of the treatments is
usually small (being formed of only 3 or 4 conjuctions),
the solutions found by ITL are considered
{\it partial solutions}.
That is, the solution doesn't offer a restriction to
every available attribute.
This is different from a typical search technique,
which offers a {\it complete solution} that assigns
a value to every input attribute.
But even though every attribute is not restricted
by ITL's solutions, the models we have used have
demonstrated small varience due to the remaining attributes.


{\it Should this idea go in the future work section?}
{\it I had this idea that by controlling the number of iterations
a user could control the amount of uncertainty.
But we haven't verified this experimental.
Should I still mention it as an idea?
Or the user could specify the acceptable
varience and iterations continue until
that varience is reached.}

Although not done yet, it might
be interesting to use other learners 
like those in \tion{contrastSet} with ITL
to see if there are any performance differences.
}
