# Self-Learning Mechanism for Mobile Game Adjustment towards a Player
## 5 Player Classification Using Neural Networks
### Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classification Measure**} & \multirow{2}{*}{**Number of Test Samples**} & \multicolumn{3}{c}{**Player Category**} \\  & & **Kid** & **Progressive** & **Senior** \\ \hline \multirow{4}{*}{Precision} & 50 & 0.959 & 0.933 & 0.967 \\  & 100 & 0.961 & 0.968 & 0.937 \\  & 150 & 0.958 & 0.958 & 0.938 \\ \hline \multirow{4}{*}{Recall} & 50 & 0.958 & 0.975 & 0.949 \\  & 100 & 0.963 & 0.957 & 0.947 \\  & 150 & 0.964 & 0.953 & 0.938 \\ \hline \multirow{4}{*}{F\({}_{1}\) score} & 50 & 0.959 & 0.954 & 0.958 \\  & 100 & 0.962 & 0.963 & 0.942 \\ \cline{1-1}  & 150 & 0.961 & 0.955 & 0.938 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Player classification results for the NN\({}_{1}\) (trained with 150 samples). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classification Measure**} & \multirow{2}{*}{**Number of Test Samples**} & \multicolumn{3}{c}{**Player Category**} \\  & & **Kid** & **Progressive** & **Senior** \\ \hline \multirow{4}{*}{Precision} & 50 & 0.973 & 0.979 & 0.936 \\  & 100 & 0.974 & 0.988 & 0.917 \\  & 150 & 0.973 & 0.983 & 0.924 \\ \hline \multirow{4}{*}{Recall} & 50 & 0.964 & 0.965 & 0.959 \\  & 100 & 0.961 & 0.967 & 0.958 \\  & 150 & 0.954 & 0.972 & 0.959 \\ \hline \multirow{4}{*}{F\({}_{1}\) score} & 50 & 0.969 & 0.972 & 0.947 \\  & 100 & 0.967 & 0.977 & 0.937 \\ \cline{1-1}  & 150 & 0.964 & 0.978 & 0.941 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Player classification results for the NN\({}_{2}\) (trained with 300 samples). The final goal of classifying a player in three defined categories is to adapt game content in line with recognized player category. Personalization of content is conducted in two ways: (1) through adequate difficulty level for each player category, and (2) through adapting the time frame set for rewarding the player, i.e., receiving the message \"Wonderful!!\". Two principal game adaptations are presented in Figure 6. The first is the level adaptation according to the recognized player category, offering more games suitable to player ability; the second is adaptation of the time frame for level reward (\"Wonderful! !\" message) based on the proposed self-learning mechanism. Additionally, the game could be adapted based on player preferences, which includes the type of images that are more often chosen and the choice to play a specific type of music or to mute it. Personalization of the game content for the three given player categories is described below. If a player is recognized as belonging to the kid category, they will receive more puzzles with difficulty level 1 and 2 (puzzles with four and nine pieces). The self-learning \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classification Measure**} & \multirow{2}{*}{**Number of Test Samples**} & \multicolumn{3}{c}{**Player Category**} \\  & & **Kid** & **Progressive** & **Senior** \\ \hline \multirow{3}{*}{Precision} & 50 & 0.939 & 0.955 & 0.983 \\  & 100 & 0.967 & 0.967 & 0.953 \\  & 150 & 0.971 & 0.975 & 0.947 \\ \hline \multirow{3}{*}{Recall} & 50 & 0.973 & 0.98 & 0.95 \\  & 100 & 0.964 & 0.958 & 0.962 \\  & 150 & 0.96 & 0.969 & 0.964 \\ \hline \multirow{3}{*}{F\({}_{1}\) score} & 50 & 0.956 & 0.967 & 0.966 \\  & 100 & 0.966 & 0.962 & 0.957 \\ \cline{1-1}  & 150 & 0.966 & 0.972 & 0.956 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Player classification results for the NN\({}_{3}\) (trained with 450 samples). Figure 6: Game adaptation according to player category. mechanism will correct the time frame for every game level by increasing the time limit so the player has an opportunity to receive the rewarding message. If a player is recognized as being in the progressive category, they will receive more puzzles with difficulty level 3 and 4 (puzzles with 16 and 25 pieces). The self-learning mechanism will correct the time frame for every game level, lowering the time limit in order to achieve a more challenging game level for the progressive player.