  Lens modeling is the key to successful and meaningful automated strong
galaxy-scale gravitational lens detection. We have implemented a lens-modeling
"robot" that treats every bright red galaxy (BRG) in a large imaging survey as
a potential gravitational lens system. Using a simple model optimized for
"typical" galaxy-scale lenses, we generate four assessments of model quality
that are used in an automated classification. The robot infers the lens
classification parameter H that a human would have assigned; the inference is
performed using a probability distribution generated from a human-classified
training set, including realistic simulated lenses and known false positives
drawn from the HST/EGS survey. We compute the expected purity, completeness and
rejection rate, and find that these can be optimized for a particular
application by changing the prior probability distribution for H, equivalent to
defining the robot's "character." Adopting a realistic prior based on the known
abundance of lenses, we find that a lens sample may be generated that is ~100%
pure, but only ~20% complete. This shortfall is due primarily to the
over-simplicity of the lens model. With a more optimistic robot, ~90%
completeness can be achieved while rejecting ~90% of the candidate objects. The
remaining candidates must be classified by human inspectors. We are able to
classify lens candidates by eye at a rate of a few seconds per system,
suggesting that a future 1000 square degree imaging survey containing 10^7
BRGs, and some 10^4 lenses, could be successfully, and reproducibly, searched
in a modest amount of time. [Abridged]
