The upper-left pane shows the output from the MISC
(Method for Inducing Structural Constraints) subsystem.
MISC read the definitions for the ten models that we characterized
and doesn't appear to have had any problems with them.
The lower-left pane lists six "Models Selected by Chosen Constraints".
Not surprisingly, these are all models that we set to Good
The right-hand pane has two sections,
containing entries for Original Constraints (from our spec)
and Suggested Constraints (that MISC created).
Each entry lists the constraint's name, type, and item (ie, process) complement.
The Suggested Constraints entries also have Select
Toggling a link, we can see the effect of its constraint.
is chosen, only the six Good
models are shown;
is chosen, all ten of the characterized models are listed:
Edit and Accept Constraint
Although MISC's proposed constraint (MISC-4
) is incomplete,
it seems like a good starting point.
After editing the constraint a bit, we can accept it for testing, etc.
Two edits are needed: add the logistic-growth
and give the constraint a meaningful name (eg, GROWTH-REQ
In summary, the Learn Constraints tab has given us a fine start;
we simply need to tweak the results.
says that exponential-growth
is a NECESSARY
but says nothing about logistic-growth
This reflects the fact that all of the models we characterized
(indeed, all of the best-performing models) include logistic-growth
So, MISC had no reason to include it in a constraint.
However, we don't want to test any
that lack the logistic-growth
Constructing and testing sub-optimal models is a waste of time
for both the computer and the modeler.
It also lowers the overall quality of the reported results.
Fortunately, this isn't a real problem, so let's get started.
Clicking on the MISC-4
constraint puts us in the Inspect Library
The right-hand pane contains the generated information:
Clicking on the Copy
link makes an editable copy of the constraint.
We can then click Edit
and create a tidied-up version.
We rename the constraint from MISC-4-COPY
then add the logistic-growth
Looking in the upper-left pane, we see that the constraint has been added:
No Suggested Constraints?
With heuristic code such as MISC, success is not always guaranteed.
Worse, it isn't always clear why a search might fail.
However, one possibility is an insufficient number of sample cases.
There are several indications that MISC has failed to find a solution:
- The upper-left pane says "0 models read".
- There are no entries shown in the lower-left pane.
- There are no Suggested Constraints in the right-hand pane.
If you see these symptoms, the first thing to try is adding more models.
Click the Go Back
button to restore your model characterization settings.
Specify settings for several more models, and try the Modify Spec button again.
If this fails, you may have generated a set of models
which MISC finds difficult to analyze.
Try running Test Models
and see if the next (pseudo-random) set works better.