Automated Generation and Balancing of Game Economies using Evolutionary Algorithms

Основні поняття

This work proposes a framework called GEEvo that uses evolutionary algorithms to generate and balance graph-based game economies, enabling efficient design and optimization of complex game economies.

Анотація

The GEEvo framework consists of two main components:

The Generator:

Generates valid game economy graphs within a simulation framework that models economies as directed graphs.
The generator uses an evolutionary algorithm to create random but valid economies, allowing control over the number and types of nodes in the generated graph.
The generator ensures the generated economies adhere to predefined constraints for each node type.

The Balancer:

Optimizes the weights of an economy's graph using an evolutionary algorithm to balance the economy towards a specified objective.
The balancer can be applied to both newly generated economies and existing ones.
It utilizes simulation-driven fitness functions that can be parameterized to balance the economy towards different objectives, such as resource generation or damage dealing.
The balancer can also handle static weights to preserve specific narrative elements during the balancing process.

The evaluation shows that the generator can create valid economies efficiently, and the balancer can optimize economies towards various balancing objectives. A case study demonstrates the balancing of damage dealing between two fictional character classes.
The GEEvo framework provides a flexible and efficient approach to support game designers in the important but time-consuming task of designing and balancing complex game economies.

Статистика

The generator could generate valid economies in 97% of cases within 50,000 iterations.
The median number of iterations required to complete was 641 (90% quantile: 9,354), with a median running time of 25 ms (90% quantile: 296 ms).
In the case study, the balancer found a solution within 2 generations (1.4 seconds) for α = 0.05 and 6 generations (16.6 seconds) for α = 0.01.

Цитати

"Game economy design significantly shapes the player experience and progression speed."
"Even a small change can have large and sometimes unforeseen effects on the overall economic behavior and therefore on the entire playing experience."

Ключові висновки, отримані з

GEEvo: Game Economy Generation and Balancing with Evolutionary Algorithms

by Florian Rupp... о arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18574.pdf

Глибші Запити

How could the GEEvo framework be extended to handle more complex game economies, such as those with dynamic edge weights or interdependent resource flows?

To handle more complex game economies with dynamic edge weights or interdependent resource flows, the GEEvo framework could be extended in several ways:

Dynamic Edge Weights: Currently, the framework focuses on optimizing static edge weights in game economies. To incorporate dynamic edge weights, the balancer algorithm could be modified to adjust weights based on changing conditions or player interactions. This would require introducing feedback loops in the simulation framework to update edge weights dynamically.

Interdependent Resource Flows: In more complex economies, resources may have interdependencies where the flow of one resource affects the availability or generation of another. The framework could be enhanced to model these interdependencies by introducing feedback mechanisms between different nodes in the economy graph. This would involve creating rules and constraints that govern how changes in one resource impact others.

Adaptive Balancing Strategies: Implementing adaptive balancing strategies that can respond to real-time changes in the game economy would be crucial. This could involve integrating machine learning algorithms within the balancer to learn and adapt to evolving conditions, ensuring optimal balance even in dynamic environments.

Probabilistic Modeling: To account for uncertainties and randomness in resource generation and transitions, probabilistic modeling could be incorporated. This would enable the framework to simulate probabilistic events and their impact on the overall economy, allowing for more realistic and nuanced balancing strategies.

By incorporating these extensions, the GEEvo framework can handle the intricacies of more complex game economies, providing game designers with a powerful tool to optimize and balance dynamic and interdependent systems effectively.

How could the GEEvo framework be integrated with player behavior models or other game design tools to provide a more holistic approach to game economy design and optimization?

Integrating the GEEvo framework with player behavior models and other game design tools can enhance the holistic approach to game economy design and optimization in the following ways:

Player Behavior Analysis: By incorporating player behavior models, the framework can simulate how different player strategies and interactions impact the game economy. This integration would allow designers to optimize the economy based on player preferences, playstyles, and engagement patterns, ensuring a more player-centric design.

Data-Driven Optimization: Leveraging player data and analytics, the framework can adapt its balancing strategies based on real-time player feedback and in-game metrics. This data-driven approach enables continuous optimization of the economy to align with player expectations and preferences.

Narrative Context Integration: Integrating narrative elements into the economy design process can enhance player immersion and engagement. By aligning the economy with the game's narrative arc and thematic elements, designers can create a more cohesive and immersive gameplay experience.

Cross-Tool Collaboration: Collaborating with other game design tools such as level editors, AI systems, and content generators can provide a comprehensive game development environment. Seamless integration between these tools allows for a unified approach to game design, where changes in one aspect (e.g., level design) can be reflected in the game economy and vice versa.

By integrating the GEEvo framework with player behavior models and other game design tools, designers can take a more holistic and data-informed approach to game economy design and optimization, ultimately enhancing the player experience and game balance.

What other balancing objectives beyond resource generation and damage dealing could be explored, and how would they impact the design of the fitness functions?

Beyond resource generation and damage dealing, several other balancing objectives could be explored in game economies, each requiring specific adaptations to the fitness functions:

Player Progression: Balancing player progression rates, experience gain, and skill acquisition can be a crucial objective. The fitness function would need to consider the pace at which players advance through the game, ensuring a smooth and engaging progression curve.

Economic Stability: Balancing the in-game economy to maintain stability, prevent inflation, and ensure a healthy flow of resources. The fitness function would focus on optimizing resource distribution, pricing mechanisms, and economic interactions to sustain a stable virtual economy.

Social Interactions: Balancing social interactions and player collaborations within the game economy. The fitness function would evaluate the impact of player interactions, trading systems, and cooperative gameplay elements on the overall balance and player experience.

Challenge Difficulty: Balancing the difficulty curve and challenge levels in the game. The fitness function would aim to adjust enemy strength, puzzle complexity, and environmental obstacles to provide an appropriate level of challenge for players at different skill levels.

Resource Scarcity: Balancing resource scarcity and abundance to create strategic decision-making opportunities for players. The fitness function would optimize resource availability, rarity, and distribution to maintain a delicate balance between scarcity and abundance.

Each of these objectives would require specific metrics, constraints, and optimization criteria in the fitness functions of the GEEvo framework. By expanding the scope of balancing objectives, designers can create more nuanced and engaging game economies that cater to diverse player preferences and gameplay styles.