Leveling-Up in Heroes of Might and Magic III

Abstract

We propose a model for level-ups in Heroes of Might and Magic III, and give an $\mathcal{O}(\frac{1}{\varepsilon^{2}} ln (\frac{1}{\delta}))$ learning algorithm to estimate the probabilities of secondary skills induced by any policy in the end of the leveling-up process. We develop software and test our model in an experiment. The correlation coefficient between theory and practice is greater than 0.99. The experiment also indicates that the process responsible for the randomization that takes place on level-ups generates only a few different pseudo-random sequences. This might allow exploitation techniques in the near future; hence that process might require reengineering.