{"title":"重复协作任务中人类适应的建模","authors":"S. Nikolaidis, S. Srinivasa","doi":"10.1145/3056540.3076211","DOIUrl":null,"url":null,"abstract":"This short paper summarizes a game-theoretic model of human partial adaptation to the robot. We model the human as following a best-response strategy to the robot action, based on theor own, possibly distorted, reward function. The model allows the robot to take informative actions, in order to teach the human its capabilities.","PeriodicalId":140232,"journal":{"name":"Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments","volume":"62 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modeling Human Adaptation in Repeated Collaborative Tasks\",\"authors\":\"S. Nikolaidis, S. Srinivasa\",\"doi\":\"10.1145/3056540.3076211\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This short paper summarizes a game-theoretic model of human partial adaptation to the robot. We model the human as following a best-response strategy to the robot action, based on theor own, possibly distorted, reward function. The model allows the robot to take informative actions, in order to teach the human its capabilities.\",\"PeriodicalId\":140232,\"journal\":{\"name\":\"Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments\",\"volume\":\"62 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3056540.3076211\",\"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 10th International Conference on PErvasive Technologies Related to Assistive Environments","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3056540.3076211","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modeling Human Adaptation in Repeated Collaborative Tasks
This short paper summarizes a game-theoretic model of human partial adaptation to the robot. We model the human as following a best-response strategy to the robot action, based on theor own, possibly distorted, reward function. The model allows the robot to take informative actions, in order to teach the human its capabilities.