Computational Red Teaming in a Sudoku Solving Context: Neural Network Based Skill Representation and Acquisition

TL;DR

This paper explores representing and acquiring Sudoku solving skills using neural networks within a computational red teaming framework, demonstrating proficiency variation through skill training.

Contribution

It introduces a neural network-based skill representation method integrated with a Sudoku solver, showing how skill acquisition affects problem-solving proficiency.

Findings

Neural networks can effectively model Sudoku solving skills.

Skill acquisition influences solver proficiency levels.

Results support developing complex skill models for red teaming.

Abstract

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.