Supporting Aim Assistance Algorithms through a Rapidly Trainable, Personalized Model of Players' Spatial and Temporal Aiming Ability

Title of the Paper

Supporting Aim Assistance Algorithms through a Rapidly Trainable, Personalized Model of Players' Spatial and Temporal Aiming Ability

Paper Information

• Subject Area: Human-Computer Interaction, Game Dynamic Balancing, Personalized Assistance Algorithms
• Keywords: Game Balance, Dynamic Difficulty Adjustment, Dynamic Player Balancing, Aim Assistance, Personalization

Research Background and Problem

• Problem or Challenge:

  • Significant differences in aiming ability among digital game players challenge fairness in multiplayer games.
  • Existing dynamic difficulty adjustment algorithms lack a systematic method to dynamically model players' aiming abilities through real-time observation of player behavior.
  • Current algorithms focus on adjusting game difficulty itself rather than accurately calibrating assistance strength based on player ability.

• Significance:

  • Games have become an important form of social interaction, and players of varying abilities should be able to participate in shared gaming experiences.
  • For individuals with disabilities, aim assistance mechanisms enable them to play with family and friends in a fair environment, promoting social equity and inclusion.

• Research Motivation:

  • To provide a data model that is both rapidly trainable and directly usable within games, serving as a tool to support dynamic aim assistance.
  • To achieve personalized aim assistance by precisely calibrating support to help players complete game tasks.

Solution

• Method and Innovation:

  • Propose a dynamic spatiotemporal model that describes players' spatial and temporal aiming abilities through difficulty parameters (e.g., target speed, size, and clickability duration).
  • The model converges quickly, requiring only a small amount of observational data to accurately predict player behavior and dynamically adapt to changes in player performance.
  • The model achieves accurate multidimensional player ability modeling by separating spatial aiming and temporal aiming.

• Implementation Steps:

  1. Observe Player Behavior: Design in-game targets and collect player aiming performance under different difficulty conditions.
  2. Train the Model: Use players' click data to fit a Gaussian distribution with maximum likelihood estimation and train model parameters through nonlinear regression.
  3. Dynamic Adjustment: Adjust target difficulty based on the trained model to achieve the desired level of player accuracy.

• Key Techniques:

  • The spatial component adopts a bivariate Gaussian distribution-based model.
  • The temporal component integrates existing cue integration models with structural optimization.
  • Linear regression is used to filter key parameters while verifying the model's training speed.

Research Outcomes

• Specific Results:

  • Designed and validated a dynamic model capable of accurately predicting players' spatial and temporal aiming performance.
  • Demonstrated through experiments that the model can be rapidly trained with minimal data (45 spatial data points and 45 temporal data points) to achieve high accuracy.
  • Identified significant effects of target difficulty parameters (e.g., width, speed, and clickability duration) on player behavior and confirmed that spatial and temporal aiming abilities are independent and separable.

• Advantages:

  • Compared to existing solutions, the model converges quickly and adapts well to individual performance.
  • Utilizes directly adjustable in-game difficulty parameters without requiring additional hardware or complex external data.

• Experimental or Evaluation Results:

  • Experiments showed that the error in predicting players' click timing was approximately 51ms, and the spatial position error was below the target size, enabling precise balancing in gaming environments.
  • The accuracy of success rate predictions was slightly lower but still within an acceptable range, with an error of approximately 14%-18%.

• Limitations and Future Directions:

  1. Limitations:
     • The study was tested on a single experimental game (ChronoSwarm), which may not comprehensively cover other gaming scenarios.
     • Online experiments might suffer from data quality issues due to participants' lack of motivation.
  2. Future Directions:
     • Apply the model to more diverse game types, including shooting games, platformers, and racing games.
     • Deploy the model in real-time gaming environments and evaluate the practical effectiveness of dynamic difficulty adjustment.
     • Explore the model's potential applications in non-gaming fields, such as assistive medical devices and remote operation systems.

This study introduces a dynamic spatiotemporal model that provides an efficient and precise method for achieving personalized game balance. Not only does it foster innovation in the gaming field, but it also holds potential for broad impacts in other application domains.