  The technique of forgetting in knowledge representation has been shown to be
a powerful and useful knowledge engineering tool with widespread application.
Yet, very little research has been done on how different policies of
forgetting, or use of different forgetting operators, affects the inferential
strength of the original theory. The goal of this paper is to define loss
functions for measuring changes in inferential strength based on intuitions
from model counting and probability theory. Properties of such loss measures
are studied and a pragmatic knowledge engineering tool is proposed for
computing loss measures using Problog. The paper includes a working methodology
for studying and determining the strength of different forgetting policies, in
addition to concrete examples showing how to apply the theoretical results
using Problog. Although the focus is on forgetting, the results are much more
general and should have wider application to other areas.
