Summary of A Framework For Predicting the Impact Of Game Balance Changes Through Meta Discovery, by Akash Saravanan and Matthew Guzdial
A Framework for Predicting the Impact of Game Balance Changes through Meta Discovery
by Akash Saravanan, Matthew Guzdial
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Meta Discovery framework uses reinforcement learning to automate the testing of balance changes in competitive games like Pokémon or League of Legends. The goal is to predict the impact of these changes on the meta game, which refers to the current dominant characters and strategies within the player base. By leveraging this framework, developers can make more informed decisions about balance changes, potentially reducing the unforeseen consequences that can occur when a change is made. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper helps game developers predict how changing rules will affect the game by using a special kind of learning called reinforcement learning. This can help developers make better choices about what changes to make and how they might affect the game. The idea is to use this framework to test different balance changes in games like Pokémon Showdown and see how well it works. |
Keywords
» Artificial intelligence » Reinforcement learning
