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Article

Adapting a Virtual Advisor’s Verbal Conversation Based on Predicted User Preferences: A Study of Neutral, Empathic and Tailored Dialogue

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Computing Department, Macquarie University, Balaclava Rd, Macquarie Park NSW 2109, Australia
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Mathematics and Statistics Department, Macquarie University, 12 Wally’s Walk, Macquarie Park NSW 2109, Australia
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College of Engineering, IT and Environment, Charles Darwin University, Ellengowan Drive, Darwin NT 0815, Australia
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INESC-ID & Institute Superior Técnico, Universisade de Lisboa, R. Alves Redol 9, 1000-029 Lisboa, Portugal
*
Author to whom correspondence should be addressed.
Multimodal Technol. Interact. 2020, 4(3), 55; https://doi.org/10.3390/mti4030055
Received: 15 July 2020 / Revised: 13 August 2020 / Accepted: 14 August 2020 / Published: 17 August 2020
(This article belongs to the Special Issue Understanding UX through Implicit and Explicit Feedback)
Virtual agents that improve the lives of humans need to be more than user-aware and adaptive to the user’s current state and behavior. Additionally, they need to apply expertise gained from experience that drives their adaptive behavior based on deep understanding of the user’s features (such as gender, culture, personality, and psychological state). Our work has involved extension of FAtiMA (Fearnot AffecTive Mind Architecture) with the addition of an Adaptive Engine to the FAtiMA cognitive agent architecture. We use machine learning to acquire the agent’s expertise by capturing a collection of user profiles into a user model and development of agent expertise based on the user model. In this paper, we describe a study to evaluate the Adaptive Engine, which compares the benefit (i.e., reduced stress, increased rapport) of tailoring dialogue to the specific user (Adaptive group) with dialogues that are either empathic (Empathic group) or neutral (Neutral group). Results showed a significant reduction in stress in the empathic and neutral groups, but not the adaptive group. Analyses of rule accuracy, participants’ dialogue preferences, and individual differences reveal that the three groups had different needs for empathic dialogue and highlight the importance and challenges of getting the tailoring right. View Full-Text
Keywords: user model; human computer interaction; virtual humans; intelligent virtual agents; rapport; study stress; virtual advisor; agent’s expertise user model; human computer interaction; virtual humans; intelligent virtual agents; rapport; study stress; virtual advisor; agent’s expertise
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MDPI and ACS Style

Ranjbartabar, H.; Richards, D.; Bilgin, A.A.; Kutay, C.; Mascarenhas, S. Adapting a Virtual Advisor’s Verbal Conversation Based on Predicted User Preferences: A Study of Neutral, Empathic and Tailored Dialogue. Multimodal Technol. Interact. 2020, 4, 55. https://doi.org/10.3390/mti4030055

AMA Style

Ranjbartabar H, Richards D, Bilgin AA, Kutay C, Mascarenhas S. Adapting a Virtual Advisor’s Verbal Conversation Based on Predicted User Preferences: A Study of Neutral, Empathic and Tailored Dialogue. Multimodal Technologies and Interaction. 2020; 4(3):55. https://doi.org/10.3390/mti4030055

Chicago/Turabian Style

Ranjbartabar, Hedieh, Deborah Richards, Ayse A. Bilgin, Cat Kutay, and Samuel Mascarenhas. 2020. "Adapting a Virtual Advisor’s Verbal Conversation Based on Predicted User Preferences: A Study of Neutral, Empathic and Tailored Dialogue" Multimodal Technologies and Interaction 4, no. 3: 55. https://doi.org/10.3390/mti4030055

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