# An Optimized Theory Revision Module

### Russell Greiner, R. Bharat Rao and Glenn Meredith

*In progress*

Essentially all theory revision systems use a set of theory-to-theory
transformations { t_k } to hill-climb from a given initial theory to a
new theory whose empirical accuracy, over a given set of labeled training
instances { q_j }, is a local optimum. At the heart of each such process is a
"evaluator", which compares the accuracy of the current theory KB with that
of each of its "neighbors" N(KB) = { t_k(KB) }, each formed by applying one
transformation to the KB, with the goal of determining which neighbor has the
highest score. One obvious way to implement such an evaluator involves simply
running each individual neighbor theory KB_k = t_k(KB) on each instance q_j;
this corresponds to the "wrapper" model [JKP'93]. In practice, however, this
approach can be prohibitively slow. We therefore built an alternative system
DD, which employs a smarter evaluator that can quickly compute the score of a
transformed theory t_k(KB), relative to the current theory KB, by "looking
inside" KB and reasoning about the effects of the t_k transformation. This
paper presents data that shows DD runs around 35 times faster than the naive
wrapped system Delta, attaining the same accuracy. We also discuss DD's
source of power, and generalize from this specific implementation to suggest
other situations to which these ideas may be applicable.