Alternating Sword Controller in First-Person Action Game using Fuzzy Logic for Adaptive Enemy: Indonesia

Key insights

6 takeaways

01
A fuzzy logic-based Dynamic Difficulty Adjustment (DDA) system successfully modulated enemy health multipliers based on player performance, with 90.5% of participants finding the difficulty adjustments appropriate.See it on page 7
02
The system utilizes smartphone gyroscope sensors as a low-cost, immersive alternative to traditional VR hardware, transmitting orientation data to a PC via Wi-Fi for real-time processing.See it on page 1
03
Performance data from 14 test subjects showed that 72% of players improved their efficiency between the first and second levels, characterized by faster completion times and higher remaining health.See it on page 6
04
The fuzzy inference system processes player duration and health metrics through nine predefined rules to automatically scale enemy resistance for subsequent game stages.See it on page 3
05
While the adaptive system effectively balanced gameplay, testing revealed a sharp difficulty spike during the transition from the second to the third level.See it on page 6
06
Future development of this Indonesian-designed action game is recommended to incorporate Human Action Recognition to further enhance player engagement and movement tracking.See it on page 7

Summary

This research introduces an adaptive first-person action game developed in Indonesia that utilizes smartphone gyroscope sensors as motion controllers to provide an immersive, low-cost alternative to traditional virtual reality hardware. The primary objective is to implement Dynamic Difficulty Adjustment (DDA) through fuzzy logic, ensuring that game challenges remain balanced to prevent player boredom or frustration. By monitoring player performance metrics—specifically duration and remaining health points—the system automatically adjusts enemy resistance levels for subsequent stages.

The methodology involves a fuzzy inference system that processes player data collected after each level. The system categorizes input variables into linguistic classes and applies a set of nine predefined fuzzy rules to calculate an output resistance value, which serves as the base health multiplier for enemies in the next level. This approach was tested with 14 respondents who played through three levels of the game. The study utilized a client-server architecture where smartphone orientation data is transmitted via Wi-Fi to a PC for real-time processing.

Key findings indicate that the fuzzy logic system effectively modulates game difficulty. Data analysis showed that 72% of players became more efficient in handling enemies between the first and second levels, evidenced by decreased completion times and stable or increased health points. While the transition from the second to the third level presented a sharper difficulty spike, the majority of respondents reported that the game was intuitive and successfully adjusted to their skill levels. Approximately 90.5% of participants found the adaptive adjustments appropriate. The research concludes that while the gyroscope-based control system is functional, future iterations could benefit from Human Action Recognition to further optimize player movement and engagement.