  In this paper we provide an insight into the skill representation, where
skill representation is seen as an essential part of the skill assessment stage
in the Computational Red Teaming process. Skill representation is demonstrated
in the context of Sudoku puzzle, for which the real human skills used in Sudoku
solving, along with their acquisition, are represented computationally in a
cognitively plausible manner, by using feed-forward neural networks with
back-propagation, and supervised learning. The neural network based skills are
then coupled with a hard-coded constraint propagation computational Sudoku
solver, in which the solving sequence is kept hard-coded, and the skills are
represented through neural networks. The paper demonstrates that the modified
solver can achieve different levels of proficiency, depending on the amount of
skills acquired through the neural networks. Results are encouraging for
developing more complex skill and skill acquisition models usable in general
frameworks related to the skill assessment aspect of Computational Red Teaming.
