  Adaptation to novelty is viewed as learning to change and augment existing
skills to confront unfamiliar situations. In this paper, we propose that the
amount of editing of an effective representation (the Representation Edit
Distance or RED) used in a set of skill programs in an agent's mental model is
a measure of difficulty for adaptation to novelty. The RED is an intuitive
approximation to the change in information content in bit strings measured by
comparing pre-novelty and post-novelty skill programs. We also present some
notional examples of how to use RED for predicting difficulty.
