{"title":"通过元发现预测游戏平衡变化影响的框架","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":"{\"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}","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}
A Framework for Predicting the Impact of Game Balance Changes Through Meta Discovery
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.