{"title":"谁教机器人促进多方社会互动?","authors":"Jouh Yeong Chew, Keisuke Nakamura","doi":"10.1145/3568294.3580056","DOIUrl":null,"url":null,"abstract":"One salient function of social robots is to play the role of facilitator to enhance the harmony state of multi-party social interactions so that every human participant is encouraged and motivated to engage actively. However, it is challenging to handcraft the behavior of social robots to achieve this objective. One promising approach is for the robot to learn from human teachers. This paper reports the findings of an empirical test to determine the optimal experiment condition for a robot to learn verbal and nonverbal strategies to facilitate a multi-party interaction. First, the modified L8 Orthogonal Array (OA) is used to design a fractional factorial experiment condition using factors like the type of human facilitator, group size and stimulus type. The response of OA is the harmony state explicitly defined using the speech turn-taking between speakers and represented using metrics extracted from the first order Markov transition matrix. Analyses of Main Effects and ANOVA suggest the type of human facilitator and group size are significant factors affecting the harmony state. Therefore, we propose the optimal experiment condition to train a facilitator robot using high school teachers as human teachers and group size larger than four participants.","PeriodicalId":36515,"journal":{"name":"ACM Transactions on Human-Robot Interaction","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Who to Teach a Robot to Facilitate Multi-party Social Interactions?\",\"authors\":\"Jouh Yeong Chew, Keisuke Nakamura\",\"doi\":\"10.1145/3568294.3580056\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One salient function of social robots is to play the role of facilitator to enhance the harmony state of multi-party social interactions so that every human participant is encouraged and motivated to engage actively. However, it is challenging to handcraft the behavior of social robots to achieve this objective. One promising approach is for the robot to learn from human teachers. This paper reports the findings of an empirical test to determine the optimal experiment condition for a robot to learn verbal and nonverbal strategies to facilitate a multi-party interaction. First, the modified L8 Orthogonal Array (OA) is used to design a fractional factorial experiment condition using factors like the type of human facilitator, group size and stimulus type. The response of OA is the harmony state explicitly defined using the speech turn-taking between speakers and represented using metrics extracted from the first order Markov transition matrix. Analyses of Main Effects and ANOVA suggest the type of human facilitator and group size are significant factors affecting the harmony state. Therefore, we propose the optimal experiment condition to train a facilitator robot using high school teachers as human teachers and group size larger than four participants.\",\"PeriodicalId\":36515,\"journal\":{\"name\":\"ACM Transactions on Human-Robot Interaction\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2023-03-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Transactions on Human-Robot Interaction\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3568294.3580056\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ROBOTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Human-Robot Interaction","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3568294.3580056","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ROBOTICS","Score":null,"Total":0}
Who to Teach a Robot to Facilitate Multi-party Social Interactions?
One salient function of social robots is to play the role of facilitator to enhance the harmony state of multi-party social interactions so that every human participant is encouraged and motivated to engage actively. However, it is challenging to handcraft the behavior of social robots to achieve this objective. One promising approach is for the robot to learn from human teachers. This paper reports the findings of an empirical test to determine the optimal experiment condition for a robot to learn verbal and nonverbal strategies to facilitate a multi-party interaction. First, the modified L8 Orthogonal Array (OA) is used to design a fractional factorial experiment condition using factors like the type of human facilitator, group size and stimulus type. The response of OA is the harmony state explicitly defined using the speech turn-taking between speakers and represented using metrics extracted from the first order Markov transition matrix. Analyses of Main Effects and ANOVA suggest the type of human facilitator and group size are significant factors affecting the harmony state. Therefore, we propose the optimal experiment condition to train a facilitator robot using high school teachers as human teachers and group size larger than four participants.
期刊介绍:
ACM Transactions on Human-Robot Interaction (THRI) is a prestigious Gold Open Access journal that aspires to lead the field of human-robot interaction as a top-tier, peer-reviewed, interdisciplinary publication. The journal prioritizes articles that significantly contribute to the current state of the art, enhance overall knowledge, have a broad appeal, and are accessible to a diverse audience. Submissions are expected to meet a high scholarly standard, and authors are encouraged to ensure their research is well-presented, advancing the understanding of human-robot interaction, adding cutting-edge or general insights to the field, or challenging current perspectives in this research domain.
THRI warmly invites well-crafted paper submissions from a variety of disciplines, encompassing robotics, computer science, engineering, design, and the behavioral and social sciences. The scholarly articles published in THRI may cover a range of topics such as the nature of human interactions with robots and robotic technologies, methods to enhance or enable novel forms of interaction, and the societal or organizational impacts of these interactions. The editorial team is also keen on receiving proposals for special issues that focus on specific technical challenges or that apply human-robot interaction research to further areas like social computing, consumer behavior, health, and education.