Guido M. Linders, Julija Vaitonyte, M. Alimardani, Kiril O. Mitev, M. Louwerse
We introduce an interactive embodied conversational agent for deployment in the healthcare sector. The agent is operated by a software architecture that integrates speech recognition, dialog management, and speech synthesis, and is embodied by a virtual human face developed using photogrammetry techniques. These features together allow for real-time, face-to-face interactions with human users. Although the developed software architecture is domain-independent and highly customizable, the virtual agent will initially be applied to healtcare domain. Here we give an overview of the different components of the architecture.
{"title":"A realistic, multimodal virtual agent for the healthcare domain","authors":"Guido M. Linders, Julija Vaitonyte, M. Alimardani, Kiril O. Mitev, M. Louwerse","doi":"10.1145/3514197.3551250","DOIUrl":"https://doi.org/10.1145/3514197.3551250","url":null,"abstract":"We introduce an interactive embodied conversational agent for deployment in the healthcare sector. The agent is operated by a software architecture that integrates speech recognition, dialog management, and speech synthesis, and is embodied by a virtual human face developed using photogrammetry techniques. These features together allow for real-time, face-to-face interactions with human users. Although the developed software architecture is domain-independent and highly customizable, the virtual agent will initially be applied to healtcare domain. Here we give an overview of the different components of the architecture.","PeriodicalId":149593,"journal":{"name":"Proceedings of the 22nd ACM International Conference on Intelligent Virtual Agents","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125600950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As intelligent agents are becoming human's teammates, not only do humans need to trust intelligent agents, but an intelligent agent should also be able to form artificial trust, i.e. a belief regarding human's trustworthiness. We see artificial trust as the beliefs of competence and willingness, and we study which internal factors (krypta) of the human may play a role when assessing artificial trust. Furthermore, we investigate which observable measures (manifesta) an agent may take into account as cues for the human teammate's krypta. This paper proposes a conceptual model of artificial trust for a specific task during human-agent teamwork. Our model proposes observable measures related to human trustworthiness (ability, benevolence, integrity) and strategy (perceived cost and benefit) as predictors for willingness and competence, based on literature and a preliminary user study.
{"title":"Assessing artificial trust in human-agent teams: a conceptual model","authors":"Carolina Centeio Jorge, M. Tielman, C. Jonker","doi":"10.1145/3514197.3549696","DOIUrl":"https://doi.org/10.1145/3514197.3549696","url":null,"abstract":"As intelligent agents are becoming human's teammates, not only do humans need to trust intelligent agents, but an intelligent agent should also be able to form artificial trust, i.e. a belief regarding human's trustworthiness. We see artificial trust as the beliefs of competence and willingness, and we study which internal factors (krypta) of the human may play a role when assessing artificial trust. Furthermore, we investigate which observable measures (manifesta) an agent may take into account as cues for the human teammate's krypta. This paper proposes a conceptual model of artificial trust for a specific task during human-agent teamwork. Our model proposes observable measures related to human trustworthiness (ability, benevolence, integrity) and strategy (perceived cost and benefit) as predictors for willingness and competence, based on literature and a preliminary user study.","PeriodicalId":149593,"journal":{"name":"Proceedings of the 22nd ACM International Conference on Intelligent Virtual Agents","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126082505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chatbots are becoming a regular part of service offerings. However, failures in human-chatbot interactions are common and mitigating them with appropriate strategies is an integral part of the dialogue. For instance, chatbots can be designed to prompt a rephrase, although, due to the complexity of user reactions, this is not always successful. Research has called for taxonomies to categorize user reactions to compute meaningful responses that encourage dialogue continuation. We suggest a framework of strategies based on prior research and test its validity, focusing on how users rephrase across dialogue turns. We find that users rephrase problems formally (49%), by changing the number of words, altering syntax, or using synonyms and to a lesser extent by altering informational value (25%). We suggest training chatbots along this behavior and designing better prompts that guide users' next actions.
{"title":"Towards a comprehensive repair framework for human-chatbot interaction: the case of rephrasing","authors":"Alina Asisof","doi":"10.1145/3514197.3549641","DOIUrl":"https://doi.org/10.1145/3514197.3549641","url":null,"abstract":"Chatbots are becoming a regular part of service offerings. However, failures in human-chatbot interactions are common and mitigating them with appropriate strategies is an integral part of the dialogue. For instance, chatbots can be designed to prompt a rephrase, although, due to the complexity of user reactions, this is not always successful. Research has called for taxonomies to categorize user reactions to compute meaningful responses that encourage dialogue continuation. We suggest a framework of strategies based on prior research and test its validity, focusing on how users rephrase across dialogue turns. We find that users rephrase problems formally (49%), by changing the number of words, altering syntax, or using synonyms and to a lesser extent by altering informational value (25%). We suggest training chatbots along this behavior and designing better prompts that guide users' next actions.","PeriodicalId":149593,"journal":{"name":"Proceedings of the 22nd ACM International Conference on Intelligent Virtual Agents","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134030689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Walter Baccinelli, S. A. van der Burg, Robin Richardson, Djura Smits, Cunliang Geng, Lars Ridder, Bouke L. Scheltinga, Nele Albers, Willem-Paul Brinkman, E. Meijer, J. Reenalda
Smoking tobacco and physical inactivity are key preventable behavioural risk factors of cardiovascular disease (CVD). Computerised coaching systems can help individuals to modify risky behaviours, thereby preventing CVD. However, most reported eHealth or computerized coaching systems are hard to reuse in slightly different settings. To provide an open-source, reusable computer coaching system, we developed Perfect Fit. The reusability is manifested by building around the open-source text- and voice-based contextual assistant framework Rasa. Rasa provides a simple, standard interface to many popular messaging and voice channels, and custom connectors are easily implemented. A set of algorithms have been developed and connected to Rasa to drive and personalize the conversation flow and the coaching process. Such algorithms make use of data stored in a devoted database. Furthermore, Perfect Fit adheres to best practices and standards in software engineering. The modular design of Perfect Fit will allow researchers to connect the virtual coach to any messaging or voice channel with only modest modification. Perfect Fit is available under open-source license in GitHub and is currently in prototype-phase. Concluding, Perfect Fit will deliver a virtual coach that can easily be adapted and reused in different settings. The coach helps individuals to achieve and maintain abstinence from smoking and sufficient physical activity (PA).
{"title":"Reusable virtual coach for smoking cessation and physical activity coaching","authors":"Walter Baccinelli, S. A. van der Burg, Robin Richardson, Djura Smits, Cunliang Geng, Lars Ridder, Bouke L. Scheltinga, Nele Albers, Willem-Paul Brinkman, E. Meijer, J. Reenalda","doi":"10.1145/3514197.3551252","DOIUrl":"https://doi.org/10.1145/3514197.3551252","url":null,"abstract":"Smoking tobacco and physical inactivity are key preventable behavioural risk factors of cardiovascular disease (CVD). Computerised coaching systems can help individuals to modify risky behaviours, thereby preventing CVD. However, most reported eHealth or computerized coaching systems are hard to reuse in slightly different settings. To provide an open-source, reusable computer coaching system, we developed Perfect Fit. The reusability is manifested by building around the open-source text- and voice-based contextual assistant framework Rasa. Rasa provides a simple, standard interface to many popular messaging and voice channels, and custom connectors are easily implemented. A set of algorithms have been developed and connected to Rasa to drive and personalize the conversation flow and the coaching process. Such algorithms make use of data stored in a devoted database. Furthermore, Perfect Fit adheres to best practices and standards in software engineering. The modular design of Perfect Fit will allow researchers to connect the virtual coach to any messaging or voice channel with only modest modification. Perfect Fit is available under open-source license in GitHub and is currently in prototype-phase. Concluding, Perfect Fit will deliver a virtual coach that can easily be adapted and reused in different settings. The coach helps individuals to achieve and maintain abstinence from smoking and sufficient physical activity (PA).","PeriodicalId":149593,"journal":{"name":"Proceedings of the 22nd ACM International Conference on Intelligent Virtual Agents","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114100720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Talya Nakash, Tom Haller, Maya Shekel, D. Pollak, Moti Lewenchuse, A. Klomek, D. Friedman
Virtual agents have been used as virtual patients for medical training, as well as for mental health training. When the training takes place inside VR the experience is more immersive, which allows for illusions of presence: the illusion that you are co-present with the virtual agent in the same space, and the illusion that the virtual agent is a real human. We have developed 'Daniel', a VR framework, based on a semi-automated virtual agent, which can be used for training for increasing resilience and for suicide prevention, and has the potential of being used as an intervention. Here we report on two different studies aimed at evaluating the framework and the psychological protocols involved. In the first study we trained participants from the general population to develop a resilience plan intervention (RPI) with a distressed virtual agent, and in the second study we trained therapists to use the safety plan intervention (SPI) with a suicidal virtual agent. In both cases we compare the VR sessions with role-playing by human actors. We report that all interventions resulted in an increase in participant self-efficacy in helping others, and we also report results on the possible importance of presence and social presence.
{"title":"Increasing resilience and preventing suicide: training and interventions with a distressed virtual human in virtual reality","authors":"Talya Nakash, Tom Haller, Maya Shekel, D. Pollak, Moti Lewenchuse, A. Klomek, D. Friedman","doi":"10.1145/3514197.3549613","DOIUrl":"https://doi.org/10.1145/3514197.3549613","url":null,"abstract":"Virtual agents have been used as virtual patients for medical training, as well as for mental health training. When the training takes place inside VR the experience is more immersive, which allows for illusions of presence: the illusion that you are co-present with the virtual agent in the same space, and the illusion that the virtual agent is a real human. We have developed 'Daniel', a VR framework, based on a semi-automated virtual agent, which can be used for training for increasing resilience and for suicide prevention, and has the potential of being used as an intervention. Here we report on two different studies aimed at evaluating the framework and the psychological protocols involved. In the first study we trained participants from the general population to develop a resilience plan intervention (RPI) with a distressed virtual agent, and in the second study we trained therapists to use the safety plan intervention (SPI) with a suicidal virtual agent. In both cases we compare the VR sessions with role-playing by human actors. We report that all interventions resulted in an increase in participant self-efficacy in helping others, and we also report results on the possible importance of presence and social presence.","PeriodicalId":149593,"journal":{"name":"Proceedings of the 22nd ACM International Conference on Intelligent Virtual Agents","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122566387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Fitrianie, Merijn Bruijnes, Fengxiang Li, Amal Abdulrahman, Willem-Paul Brinkman
We present the ASA Questionnaire, an instrument for evaluating human interaction with an artificial social agent (ASA), resulting from multi-year efforts involving more than 100 Intelligent Virtual Agent (IVA) researchers worldwide. It has 19 measurement constructs constituted by 90 items, which capture more than 80% of the constructs identified in empirical studies published in the IVA conference 2013--2018. This paper reports on construct validity analysis, specifically convergent and discriminant validity of initial 131 instrument items that involved 532 crowd-workers who were asked to rate human interaction with 14 different ASAs. The analysis included several factor analysis models and resulted in the selection of 90 items for inclusion in the long version of the ASA questionnaire. In addition, a representative item of each construct or dimension was selected to create a 24-item short version of the ASA questionnaire. Whereas the long version is suitable for a comprehensive evaluation of human-ASA interaction, the short version allows quick analysis and description of the interaction with the ASA. To support reporting ASA questionnaire results, we also put forward an ASA chart. The chart provides a quick overview of the agent profile.
{"title":"The artificial-social-agent questionnaire: establishing the long and short questionnaire versions","authors":"S. Fitrianie, Merijn Bruijnes, Fengxiang Li, Amal Abdulrahman, Willem-Paul Brinkman","doi":"10.1145/3514197.3549612","DOIUrl":"https://doi.org/10.1145/3514197.3549612","url":null,"abstract":"We present the ASA Questionnaire, an instrument for evaluating human interaction with an artificial social agent (ASA), resulting from multi-year efforts involving more than 100 Intelligent Virtual Agent (IVA) researchers worldwide. It has 19 measurement constructs constituted by 90 items, which capture more than 80% of the constructs identified in empirical studies published in the IVA conference 2013--2018. This paper reports on construct validity analysis, specifically convergent and discriminant validity of initial 131 instrument items that involved 532 crowd-workers who were asked to rate human interaction with 14 different ASAs. The analysis included several factor analysis models and resulted in the selection of 90 items for inclusion in the long version of the ASA questionnaire. In addition, a representative item of each construct or dimension was selected to create a 24-item short version of the ASA questionnaire. Whereas the long version is suitable for a comprehensive evaluation of human-ASA interaction, the short version allows quick analysis and description of the interaction with the ASA. To support reporting ASA questionnaire results, we also put forward an ASA chart. The chart provides a quick overview of the agent profile.","PeriodicalId":149593,"journal":{"name":"Proceedings of the 22nd ACM International Conference on Intelligent Virtual Agents","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117074724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
James H. Hale, Harshit Jalan, Nidhi Saini, Shao Ling Tan, Junhyuck Woo, J. Gratch
Much prior work in automated negotiation makes the simplifying assumption of linear utility functions. As such, we propose a framework for multilateral repeated negotiations in a complex game setting---to introduce non-linearities---where negotiators can choose with whom they negotiate in subsequent games. This game setting not only creates non-linear utility functions, but also motivates the negotiation.
{"title":"Negotiation game to introduce non-linear utility","authors":"James H. Hale, Harshit Jalan, Nidhi Saini, Shao Ling Tan, Junhyuck Woo, J. Gratch","doi":"10.1145/3514197.3549678","DOIUrl":"https://doi.org/10.1145/3514197.3549678","url":null,"abstract":"Much prior work in automated negotiation makes the simplifying assumption of linear utility functions. As such, we propose a framework for multilateral repeated negotiations in a complex game setting---to introduce non-linearities---where negotiators can choose with whom they negotiate in subsequent games. This game setting not only creates non-linear utility functions, but also motivates the negotiation.","PeriodicalId":149593,"journal":{"name":"Proceedings of the 22nd ACM International Conference on Intelligent Virtual Agents","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114814886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ronald Cumbal, Daniel Alexander Kazzi, Vincent Winberg, Olov Engwall
Non-verbal cues used in human communication have shown to be efficient in shaping speaking interactions. When applied to virtual agents or social robots, results imply that a similar effect is expected in dyad settings. In this study, we explore how encouraging, vocal and non-vocal, backchannels can stimulate speaking participation in a game-based multi-party interaction, where unbalanced contribution is expected. We design the study using a social robot, taking part in a language game with native speakers and language learners, to evaluate how an adaptive generation of backchannels, that targets the least speaking participant to encourage more speaking contribution, affects the group and individual participant's behavior. We report results from experiments with 30 subjects divided in pairs assigned to the adaptive (encouraging) and (neutral) control condition. Our results show that the speaking participation of the least active speaker increases significantly when the robot uses an adaptive backchanneling strategy. At the same time, the participation of the more active speaker slightly decreases, which causes the combined speaking time of both participants to be similar between the Control and Experimental conditions. The adaptive strategy further leads to a 50% decrease in the difference in speaker shares between the two participants (indicating a more balanced participation) compared to the Control condition. However, this distribution between speaker ratios is not significantly different from the Control.
{"title":"Shaping unbalanced multi-party interactions through adaptive robot backchannels","authors":"Ronald Cumbal, Daniel Alexander Kazzi, Vincent Winberg, Olov Engwall","doi":"10.1145/3514197.3549680","DOIUrl":"https://doi.org/10.1145/3514197.3549680","url":null,"abstract":"Non-verbal cues used in human communication have shown to be efficient in shaping speaking interactions. When applied to virtual agents or social robots, results imply that a similar effect is expected in dyad settings. In this study, we explore how encouraging, vocal and non-vocal, backchannels can stimulate speaking participation in a game-based multi-party interaction, where unbalanced contribution is expected. We design the study using a social robot, taking part in a language game with native speakers and language learners, to evaluate how an adaptive generation of backchannels, that targets the least speaking participant to encourage more speaking contribution, affects the group and individual participant's behavior. We report results from experiments with 30 subjects divided in pairs assigned to the adaptive (encouraging) and (neutral) control condition. Our results show that the speaking participation of the least active speaker increases significantly when the robot uses an adaptive backchanneling strategy. At the same time, the participation of the more active speaker slightly decreases, which causes the combined speaking time of both participants to be similar between the Control and Experimental conditions. The adaptive strategy further leads to a 50% decrease in the difference in speaker shares between the two participants (indicating a more balanced participation) compared to the Control condition. However, this distribution between speaker ratios is not significantly different from the Control.","PeriodicalId":149593,"journal":{"name":"Proceedings of the 22nd ACM International Conference on Intelligent Virtual Agents","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128252209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Georgiana Cristina Dobre, M. Gillies, David C. Ranyard, Russell J. Harding, Xueni Pan
People can understand how human interaction unfolds and can pinpoint social attitudes such as showing interest or social engagement with a conversational partner. However, summarising this with a set of rules is difficult, as our judgement is sometimes subtle and subconscious. Hence, it is challenging to program agents or non-player characters (NPCs) to react towards social signals appropriately, which is important for immersive narrative games in Virtual Reality (VR). We present a collaborative work between two game studios (Maze Theory and Dream Reality Interactive) and academia to develop an immersive machine learning (ML) pipeline for detecting social engagement. Here we introduce the motivation and the methodology of the immersive ML pipeline, then we cover the motivation for the industry-academia collaboration, how it progressed, the implications of joined work on the industry and reflective insights on the collaboration. Overall, we highlight the industry-academia collaborative work on an immersive ML pipeline for detecting social engagement. We demonstrate how creatives could use ML and VR to expand their ability to design more engaging commercial games.
{"title":"More than buttons on controllers: engaging social interactions in narrative VR games through social attitudes detection","authors":"Georgiana Cristina Dobre, M. Gillies, David C. Ranyard, Russell J. Harding, Xueni Pan","doi":"10.1145/3514197.3551496","DOIUrl":"https://doi.org/10.1145/3514197.3551496","url":null,"abstract":"People can understand how human interaction unfolds and can pinpoint social attitudes such as showing interest or social engagement with a conversational partner. However, summarising this with a set of rules is difficult, as our judgement is sometimes subtle and subconscious. Hence, it is challenging to program agents or non-player characters (NPCs) to react towards social signals appropriately, which is important for immersive narrative games in Virtual Reality (VR). We present a collaborative work between two game studios (Maze Theory and Dream Reality Interactive) and academia to develop an immersive machine learning (ML) pipeline for detecting social engagement. Here we introduce the motivation and the methodology of the immersive ML pipeline, then we cover the motivation for the industry-academia collaboration, how it progressed, the implications of joined work on the industry and reflective insights on the collaboration. Overall, we highlight the industry-academia collaborative work on an immersive ML pipeline for detecting social engagement. We demonstrate how creatives could use ML and VR to expand their ability to design more engaging commercial games.","PeriodicalId":149593,"journal":{"name":"Proceedings of the 22nd ACM International Conference on Intelligent Virtual Agents","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127450193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mukesh Barange, Sandratra Rasendrasoa, Maël Bouabdelli, Julien Saunier, A. Pauchet
Empathic behavior between humans often has a positive effect, particularly in healthcare, since it facilitates relationships, improves engagement, and reduces stress and anxiety. Despite the importance of empathic communication and social relationship in healthcare, effects of empathic behavior of embodied virtual agents that interact with patients in a multimodal and adaptive way have not been widely explored. In this article, we propose an empathic model which endows a therapeutic embodied virtual agent with multi-modal adaptive empathic behavior during interaction with a user. This model relies on the user-agent interaction relationship and focuses on (1) the interpretation of user's behavior using multimodal input, and (2) the generation of multimodal empathic behavior during interaction. An experimental study in the context of empathic interaction with students during the COVID-19 pandemic is presented to evaluate the effects of adaptive empathic behavior of an agent on the quality of user interaction. Compared to an agent that relies on low-level affect matching and backchannels, results show that our agent is perceived as more empathic and improves user engagement during the interaction.
{"title":"Impact of adaptive multimodal empathic behavior on the user interaction","authors":"Mukesh Barange, Sandratra Rasendrasoa, Maël Bouabdelli, Julien Saunier, A. Pauchet","doi":"10.1145/3514197.3549675","DOIUrl":"https://doi.org/10.1145/3514197.3549675","url":null,"abstract":"Empathic behavior between humans often has a positive effect, particularly in healthcare, since it facilitates relationships, improves engagement, and reduces stress and anxiety. Despite the importance of empathic communication and social relationship in healthcare, effects of empathic behavior of embodied virtual agents that interact with patients in a multimodal and adaptive way have not been widely explored. In this article, we propose an empathic model which endows a therapeutic embodied virtual agent with multi-modal adaptive empathic behavior during interaction with a user. This model relies on the user-agent interaction relationship and focuses on (1) the interpretation of user's behavior using multimodal input, and (2) the generation of multimodal empathic behavior during interaction. An experimental study in the context of empathic interaction with students during the COVID-19 pandemic is presented to evaluate the effects of adaptive empathic behavior of an agent on the quality of user interaction. Compared to an agent that relies on low-level affect matching and backchannels, results show that our agent is perceived as more empathic and improves user engagement during the interaction.","PeriodicalId":149593,"journal":{"name":"Proceedings of the 22nd ACM International Conference on Intelligent Virtual Agents","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121078157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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