{"title":"A Framework for Predicting the Impact of Game Balance Changes Through Meta Discovery","authors":"Akash Saravanan;Matthew Guzdial","doi":"10.1109/TG.2024.3457822","DOIUrl":null,"url":null,"abstract":"A metagame is a collection of knowledge that goes beyond the rules of a game. In competitive, team-based games, such as \n<italic>Pokémon</i>\n or \n<italic>League of Legends</i>\n, 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 article, 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 \n<italic>Pokémon Showdown</i>\n, a collection of competitive \n<italic>Pokémon</i>\n tiers, with high accuracy.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"16 4","pages":"821-830"},"PeriodicalIF":1.7000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Games","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10675455/","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
A metagame is a collection of knowledge that goes beyond the rules of a game. In competitive, team-based games, such as
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 article, 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.