The Decision Tree Thompson Sampling (DTTS) algorithm is a powerful tool used in web-based web games to dynamically personalize the gaming experience for each player. It is a type of Multi-Armed Bandit (MAB) algorithm that optimizes resource allocation and content delivery to address the unique needs and preferences of individual players.
- Clone repository:
git clone https://github.com/ChugxScript/MAB_-_Algebruh.git
- Install Node.js and npm:
- If Node.js and npm are not already installed on your system, download and install them from the official website.
- Install Firebase CLI:
npm install -g firebase-tools
- Install Project Dependencies:
npm install
- Run
index.htmlin the 'public' folder through Visual Studio Code Live Server. - Change the IP address to
localhost.
- Personalization: The DTTS algorithm leverages player data collected during gameplay, such as user interactions, preferences, and performance metrics.
- Decision Making: Based on the collected data, the algorithm dynamically adapts the game environment, including difficulty levels, rewards, and challenges, to maximize player engagement and satisfaction.
- Question Difficulty Selection: One key aspect of the DTTS algorithm is its ability to determine the appropriate difficulty level for questions presented to players. This decision is made based on the player's past performance and behavior within the game.
- Thompson Sampling: DTTS employs the Thompson Sampling approach, a Bayesian probability technique, to balance exploration and exploitation. It continuously learns from player interactions to make informed decisions about which game features to present to each player.
- Optimization: By optimizing resource allocation and content delivery, DTTS enhances player retention, increases game enjoyment, and ultimately boosts overall user satisfaction.
- Personalized Experience: DTTS tailors the gaming experience to the preferences and behaviors of individual players, enhancing engagement and immersion.
- Efficient Resource Allocation: By dynamically adjusting game elements, DTTS optimizes the allocation of resources, such as in-game rewards and challenges, to maximize player satisfaction.
- Continuous Learning: The algorithm continuously learns from player interactions, allowing for adaptive gameplay that evolves over time to meet changing player preferences and skill levels.
- Data Collection: DTTS relies on comprehensive data collection mechanisms to gather information about player behavior, preferences, and performance metrics.
- Algorithm Integration: The DTTS algorithm is integrated into the game backend, where it analyzes player data in real-time and makes dynamic adjustments to the game environment.
- User Interface: The personalized gaming experience delivered by DTTS is manifested through the game's user interface, where players encounter tailored challenges, rewards, and content based on their individual profiles.