A Framework for Predicting the Impact of Game Balance Changes through Meta Discovery
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912022806921216 |
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| author | Saravanan, Akash Guzdial, Matthew |
| author_facet | Saravanan, Akash Guzdial, Matthew |
| contents | A metagame is a collection of knowledge that goes beyond the rules of a game. In competitive, team-based games like Pokémon or League of Legends, it refers to the set of current dominant characters and/or strategies within the player base. Developer changes to the balance of the game can have drastic and unforeseen consequences on these sets of meta characters. A framework for predicting the impact of balance changes could aid developers in making more informed balance decisions. In this paper we present such a Meta Discovery framework, leveraging Reinforcement Learning for automated testing of balance changes. Our results demonstrate the ability to predict the outcome of balance changes in Pokémon Showdown, a collection of competitive Pokémon tiers, with high accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_07340 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Framework for Predicting the Impact of Game Balance Changes through Meta Discovery Saravanan, Akash Guzdial, Matthew Artificial Intelligence Machine Learning A metagame is a collection of knowledge that goes beyond the rules of a game. In competitive, team-based games like Pokémon or League of Legends, it refers to the set of current dominant characters and/or strategies within the player base. Developer changes to the balance of the game can have drastic and unforeseen consequences on these sets of meta characters. A framework for predicting the impact of balance changes could aid developers in making more informed balance decisions. In this paper we present such a Meta Discovery framework, leveraging Reinforcement Learning for automated testing of balance changes. Our results demonstrate the ability to predict the outcome of balance changes in Pokémon Showdown, a collection of competitive Pokémon tiers, with high accuracy. |
| title | A Framework for Predicting the Impact of Game Balance Changes through Meta Discovery |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2409.07340 |