Dimitris Vatsakis, Paris Mavromoustakos-Blom, P. Spronck
{"title":"一个互联网辅助的玩dixit的AI","authors":"Dimitris Vatsakis, Paris Mavromoustakos-Blom, P. Spronck","doi":"10.1145/3555858.3555863","DOIUrl":null,"url":null,"abstract":"This paper investigates the development of an Artificial Intelligence (AI) agent which plays the voting phase of the board game Dixit. Given a set of open cards and a lexical “hint” provided by a player, our algorithm aims to predict which card the hint originally refers to. The AI agent is developed using Machine Learning (ML) algorithms for Natural Language Processing (NLP). The AI agent is equipped with models that explore data of human-played games and retrieves information from the internet to deal with any shortage of information. We show that the Dixit AI agent we developed is more accurate than the average human player in finding the card which corresponds to a hint.","PeriodicalId":290159,"journal":{"name":"Proceedings of the 17th International Conference on the Foundations of Digital Games","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"An Internet-assisted Dixit-playing AI\",\"authors\":\"Dimitris Vatsakis, Paris Mavromoustakos-Blom, P. Spronck\",\"doi\":\"10.1145/3555858.3555863\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper investigates the development of an Artificial Intelligence (AI) agent which plays the voting phase of the board game Dixit. Given a set of open cards and a lexical “hint” provided by a player, our algorithm aims to predict which card the hint originally refers to. The AI agent is developed using Machine Learning (ML) algorithms for Natural Language Processing (NLP). The AI agent is equipped with models that explore data of human-played games and retrieves information from the internet to deal with any shortage of information. We show that the Dixit AI agent we developed is more accurate than the average human player in finding the card which corresponds to a hint.\",\"PeriodicalId\":290159,\"journal\":{\"name\":\"Proceedings of the 17th International Conference on the Foundations of Digital Games\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 17th International Conference on the Foundations of Digital Games\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3555858.3555863\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 17th International Conference on the Foundations of Digital Games","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3555858.3555863","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper investigates the development of an Artificial Intelligence (AI) agent which plays the voting phase of the board game Dixit. Given a set of open cards and a lexical “hint” provided by a player, our algorithm aims to predict which card the hint originally refers to. The AI agent is developed using Machine Learning (ML) algorithms for Natural Language Processing (NLP). The AI agent is equipped with models that explore data of human-played games and retrieves information from the internet to deal with any shortage of information. We show that the Dixit AI agent we developed is more accurate than the average human player in finding the card which corresponds to a hint.