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UX Framework Including Imbalanced UX Dataset Reduction Method for Analyzing Interaction Trends of Agent Systems.
1Department of Multimedia Engineering, Graduate School, Dongguk University-Seoul, Seoul 04620, Republic of Korea.
Sensors (Basel, Switzerland)
|February 11, 2023
Summary
This study introduces a user experience (UX) framework using data reduction to personalize game AI agents. It minimizes data loss for individual users, enhancing their interaction and game experience.
Area of Science:
- Computer Science
- Human-Computer Interaction
- Artificial Intelligence
Background:
- Game AI performance influences user purchasing decisions.
- User experience (UX) technology analyzes user interface (UI) interactions to gauge game AI satisfaction.
- Traditional UX systems identify general user trends but struggle with individual user data analysis.
Purpose of the Study:
- To propose a novel UX framework for game agent systems.
- To enhance individual user interaction through personalized game agents.
- To minimize the loss of UX data features for each user.
Main Methods:
- Application of a UX data reduction method within the game agent framework.
- Maintaining non-trend data features in UX datasets to prevent overfitting.
- Developing personalized game agents by reflecting individual user interaction trends.
Main Results:
- The proposed UX framework was applied to the game "Freestyle".
- Minimized overfitting in an imbalanced UX dataset, creating a user-specific interaction trend dataset.
- Generated UX datasets enabled customized game agents for enhanced user interaction.
Conclusions:
- The proposed UX framework effectively personalizes game agents by minimizing UX data feature loss.
- The framework improves user interaction by reflecting individual trends and reducing overfitting.
- This research is expected to advance UX-based personalized services in gaming and beyond.
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