## 3 Materials and Methods
### Model Training  
Once the player metrics have been recollected during the training phase, a model can be trained using DL techniques to apply it to the obtained metrics. Finally, the game should evaluate the events performed by the players and classify their level of familiarity with the game into three categories: beginner, intermediate, and expert. By categorizing players according to their expertise levels, gameplay mechanics can be adjusted to provide a better experience for all players.  
Figure 4 illustrates the process of utilizing DL techniques to analyze player metrics. This process is divided into three main paths.  
Path 1 represents the traditional method for evaluating a player's performance in a game scenario. First, the players' performance metrics were measured, and the results were obtained based on these metrics.  
Path 2 represents the data collection and training processes. First, the player plays the scenario, and the performance metrics are collected to create a dataset. The dataset was then used to train a deep learning model. Finally, model evaluation was conducted, and if the model performance was satisfactory, it was compiled and used for further analysis.  
Path 3 represents an alternative approach when model evaluation results are unsatisfactory. In this case, technical analysis was conducted to identify the shortcomings of the model. The dataset was then further refined based on this analysis, and the model training  
Figure 4: Data collection and training flow in Astro.  
and evaluation process was repeated until satisfactory results were obtained. The compiled model was then used for further analysis.  
The process involves data collection, model training, and evaluation, and may require multiple iterations to achieve satisfactory results.  
Deep learning has shown great potential for creating models that can adapt to the changing skill levels of players in a game. By analyzing player metrics, such as the bullet number in contact with an alien, accuracy, and game progress, neural nodes can be trained to dynamically adjust the complexity of the game to enhance the player's experience and provide a personalized gameplay experience that caters to the individual's skill level.  
Once the model is validated, it must be implemented in a game. Figure 5 illustrates how integrating the trained model into Astro expedites the game level adjustment process. The diagram begins with the initial level of the game.  
As the player proceeded through the game scenario from the initial game level, their performance metrics were recorded. If the player completes the scenario successfully, the process advances to the \"Scenario completed (Yes)\" point.  
At this juncture, the trained model leveraged the player's performance metrics to calibrate the game level. Subsequently, the game presents an adjusted game level scenario for the player, and the performance metrics are measured again. If a player succeeds in the scenario, the model further tailors the game level. This cycle continues until the player can no longer complete the scenario, and the process proceeds to the \"Scenario completed (No)\" point.  
The game level adjustment process concludes by presenting the outcomes to players. Finally, the player can restart or finalize the game.  
Scatter plots were generated for each attribute in the original dataset (Figure 6). Notably, patterns emerged concerning the precision of players' ship movement events. The  
Figure 5: Astro game flow with dynamic complexity adjustment.  
relationship between the main attributes of the dataset can be observed in the case of collisions (_hit_), where a linear relationship with the rest of the attributes can be identified in the central column. However, for the level (_lvl_) in the right column of the same Figure, a discernible pattern that would allow us to prioritize the attribute cannot be identified.