Damage Optimization in Video Games: A Player-Driven Co-Creative Approach

Title of the Paper

Damage Optimization in Video Games: A Player-Driven Co-Creative Approach

Paper Information

• Field of Study: Game analysis and optimization algorithms, specifically damage optimization in video games
• Keywords: Damage optimization, video game simulation, game analysis, autonomous learning, player co-creation

Research Background and Problem Statement

• Identified Problems or Challenges:

  1. Damage mechanics in video games are complex, making damage output optimization one of the main challenges for players.
  2. Players often spend significant time building theoretical models and learning rotation strategies, which hinders their ability to quickly find optimal output strategies.
  3. Game developers face difficulties in predicting and optimizing complex damage mechanics when designing and balancing games.
  4. Steep learning curves make it challenging for novice players to master damage optimization, leading to frustration, while advanced players require more efficient optimization tools.

• Significance:
  Damage optimization directly impacts player experience, game acceptance, and retention rates, as well as game balance, especially in competitive and multiplayer games.

• Research Motivation:
  Traditionally, players rely on community-shared theoretical builds to improve damage output; however, this approach is inefficient and burdensome to learn. The authors aim to develop an interactive damage optimization tool to help players optimize game strategies and enhance output while addressing developers' challenges in designing and balancing game mechanics.

Proposed Solution

• Main Methods:

  1. Propose a general damage optimization framework applicable to various game scenarios.
  2. Develop a simulation-based backend to measure and analyze all relevant in-game behavioral data.
  3. Combine AI-driven reward maximization algorithms with interactive visualization tools to help users intuitively understand damage optimization strategies.

• Innovations:

  • Model player behavior as a series of quantifiable and optimizable actions.
  • Provide an interactive visualization dashboard to support users in exploring and comparing different damage strategies, including AI and manual optimizations.
  • Offer a general-purpose tool for both players and developers, balancing precision and applicability in damage calculation.

• Implementation Steps and Key Technologies:

  1. Damage Simulator:
     • Follows general tree search and Markov decision process methods to simulate damage events.
     • Configurable using JSON format.
     • Supports modeling for both single-player and multi-character interaction scenarios.
  2. Frontend Visualization Tool:
     • Provides a rotation configuration interface with text, graphical, and node-based modes.
     • Offers various analytical outputs, such as damage distribution and priority rankings, to help users understand key factors affecting damage.
  3. AI Component:
     • Utilizes heuristic search and breadth-first search to optimize player rotation strategies.
     • Allows manual adjustment of reward parameters, enhancing user control over the optimization process.

Research Outcomes

• Specific Achievements:

  • Developed a general-purpose damage optimization tool, "Rotation Optimizer," and conducted a case study in the MMORPG Guild Wars 2.
  • Achieved a simulation-to-real damage output discrepancy of less than 1%, ensuring high precision.
  • The tool accurately predicted multiple outcomes of game balance updates, with an accuracy rate of 99%.

• Advantages Compared to Existing Solutions:

  • Unlike traditional damage tools based on empirical rules (e.g., World of Warcraft's SimulationCraft), this tool supports interactive exploration and integrates AI optimization.
  • Highly scalable and not restricted to specific game types.
  • Innovatively combines descriptive, diagnostic, predictive, and prescriptive analytics to enhance active learning.

• Experimental and Evaluation Results:

  • The tool was well-received in the player community, with significant improvements in damage output performance among its users.
    • Under ideal conditions, users' average performance improved by 9.7%.
    • In actual boss fights, users' performance improved by approximately 6.3%.
  • Player feedback was generally positive, with advanced players noting the tool's particular usefulness for beginners and intermediate players.
  • User satisfaction scored 70.6 out of 100, indicating good usability.

• Limitations and Future Directions:

  • Currently supports only deterministic simulations; future work should incorporate support for random factors, such as exploring multiple output paths.
  • The optimization effects in multi-player interaction scenarios remain underexplored.
  • The low proportion of beginner players in the community suggests a need to lower the tool's usage threshold.
  • Plans to expand tool functionality, such as integrating equipment optimization tools.

Conclusion

This study provides a comprehensive solution for damage optimization in video games, bridging academic gaps through technical simulation and player collaboration while positively impacting game design and experience. The research demonstrates that data-driven player tools can not only optimize game performance but also assist developers in improving balance and enhancing players' understanding of complex mechanics.