L. F. Góes, Alysson Ribeiro da Silva, João Saffran, Alvaro Amorim, Celso França, Tiago Zaidan, Bernardo M. P. Olímpio, L. O. Alves, Hugo Morais, Shirley Luana, Carlos Martins
{"title":"HoningStone: Building Creative Combos With Honing Theory for a Digital Card Game","authors":"L. F. Góes, Alysson Ribeiro da Silva, João Saffran, Alvaro Amorim, Celso França, Tiago Zaidan, Bernardo M. P. Olímpio, L. O. Alves, Hugo Morais, Shirley Luana, Carlos Martins","doi":"10.1109/TCIAIG.2016.2536689","DOIUrl":null,"url":null,"abstract":"In recent years, online digital games have left behind the status of entertainment sources to become also professional electronic sports. Worldwide championships offer prizes up to millions of dollars for the best competitors and/or teams among different game categories such as digital collectible card games (DCCG), multiplayer online battle arena, etc. Hearthstone, by Blizzard Entertainment, is a DCCG that has an increasing number of players up to the millions. In this game, individual players compete in one-versus-one matches in alternating turns, until a player is defeated. The greatest challenge in this game is to build a deck of cards and a strategy to combine these cards in order to be competitive against other players without a priori knowledge about their decks and strategies. This is a daunting task that requires deep knowledge of each existing card and great amount of creativity to surprise adversaries in this very adaptive environment. This paper presents a computational system, called HoningStone, that automatically generates creative card combos based on the honing theory of creativity. Our experimental results show that HoningStone can generate combos that are more creative than a greedy randomized algorithm driven by a creativity metric.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"9 1","pages":"204-209"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2536689","citationCount":"17","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Computational Intelligence and AI in Games","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TCIAIG.2016.2536689","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 17
Abstract
In recent years, online digital games have left behind the status of entertainment sources to become also professional electronic sports. Worldwide championships offer prizes up to millions of dollars for the best competitors and/or teams among different game categories such as digital collectible card games (DCCG), multiplayer online battle arena, etc. Hearthstone, by Blizzard Entertainment, is a DCCG that has an increasing number of players up to the millions. In this game, individual players compete in one-versus-one matches in alternating turns, until a player is defeated. The greatest challenge in this game is to build a deck of cards and a strategy to combine these cards in order to be competitive against other players without a priori knowledge about their decks and strategies. This is a daunting task that requires deep knowledge of each existing card and great amount of creativity to surprise adversaries in this very adaptive environment. This paper presents a computational system, called HoningStone, that automatically generates creative card combos based on the honing theory of creativity. Our experimental results show that HoningStone can generate combos that are more creative than a greedy randomized algorithm driven by a creativity metric.
期刊介绍:
Cessation. The IEEE Transactions on Computational Intelligence and AI in Games (T-CIAIG) publishes archival journal quality original papers in computational intelligence and related areas in artificial intelligence applied to games, including but not limited to videogames, mathematical games, human–computer interactions in games, and games involving physical objects. Emphasis is placed on the use of these methods to improve performance in and understanding of the dynamics of games, as well as gaining insight into the properties of the methods as applied to games. It also includes using games as a platform for building intelligent embedded agents for the real world. Papers connecting games to all areas of computational intelligence and traditional AI are considered.