Self-Adapting Chatbot Personalities for Better Peer Support

Yorita, Akihiro; Egerton, Simon; Oakman, Jodi; Chan, Carina Ka Yee; Kubota, Naoyuki

doi:10.1109/smc.2019.8914583

Cited by 20 publications

(8 citation statements)

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“…While the intelligence framework classifies the entire conversation process, the intelligence quotient dimension describes the intelligence in evaluating a single response. The personality processing D 3 characterizes the capacity of the chatbot to emphatically tailor its notation responses to the specific personality and mood of the user by identifying the personality trait of the counterpart (Di Prospero et al 2017;Yorita et al 2019). The chatbot adapts to the real-time identified personality trait of the user (Yorita et al 2019).…”

Section: Intelligencementioning

confidence: 99%

“…The personality processing D 3 characterizes the capacity of the chatbot to emphatically tailor its notation responses to the specific personality and mood of the user by identifying the personality trait of the counterpart (Di Prospero et al 2017;Yorita et al 2019). The chatbot adapts to the real-time identified personality trait of the user (Yorita et al 2019). The socio-emotional behavior D 4 characterizes the resonance capacity of the chatbot to show affection or empathy towards the individual needs and immediate feedback of the user, which the user reveals through resonating emotions within a dialogue (Bittner et al 2019;Yalçın 2019).…”

Section: Intelligencementioning

confidence: 99%

“…The area of emotional processing is currently being studied in the scientific community from various perspectives. Hu et al (2018) observed that a passionate and empathetic tone, compared to six other tones of a chatbot, increases the user experience, while Yorita et al (2019) concluded that the reaction of the user depends strongly on the design of the socio-emotional skillset of the chatbot. Therefore, various researchers (e.g., Yalçın 2019;Yorita et al 2019;Rouast et al 2019) focus on automatic affect recognition of chatbot user's personality and emotional state to adapt to it.…”

Section: Theoretical and Practical Implicationsmentioning

confidence: 99%

“…Hu et al (2018) observed that a passionate and empathetic tone, compared to six other tones of a chatbot, increases the user experience, while Yorita et al (2019) concluded that the reaction of the user depends strongly on the design of the socio-emotional skillset of the chatbot. Therefore, various researchers (e.g., Yalçın 2019;Yorita et al 2019;Rouast et al 2019) focus on automatic affect recognition of chatbot user's personality and emotional state to adapt to it. The two design elements socio-emotional behavior and personality processing are of great importance for the user experience and acceptance of chatbots (Jain et al 2018).…”

Section: Theoretical and Practical Implicationsmentioning

confidence: 99%

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Virtual Assistance in Any Context

et al. 2020

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Several domain-specific assistants in the form of chatbots have conquered many commercial and private areas. However, there is still a limited level of systematic knowledge of the distinctive characteristics of design elements for chatbots to facilitate development, adoption, implementation, and further research. To close this gap, the paper outlines a taxonomy of design elements for chatbots with 17 dimensions organized into the perspectives intelligence, interaction and context. The conceptually grounded design elements of the taxonomy are used to analyze 103 chatbots from 23 different application domains. Through a clustering-based approach, five chatbot archetypes that currently exist for domain-specific chatbots are identified. The developed taxonomy provides a structure to differentiate and categorize domain-specific chatbots according to archetypal qualities that guide practitioners when taking design decisions. Moreover, the taxonomy serves academics as a foundation for conducting further research on chatbot design while integrating scientific and practical knowledge.Keywords Chatbot taxonomy Á Design elements Á Domain-specific chatbots Á Human computer interaction Bus Inf Syst Eng 62(3): 211-225 (2020)

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Section: Intelligencementioning

confidence: 99%

Section: Intelligencementioning

confidence: 99%

Section: Theoretical and Practical Implicationsmentioning

confidence: 99%

Section: Theoretical and Practical Implicationsmentioning

confidence: 99%

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Virtual Assistance in Any Context

et al. 2020

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“…Although these tendencies differ among individuals, psychological studies have succeeded in modeling the tendencies with a limited number of variables [1,2]. Such information is useful for improving the evaluation of interactions using communication robots and chatbots [3,4]. For example, if a person is extrovert, a robot can talk to the person actively to improve the satisfaction of their interaction.…”

Section: Introductionmentioning

confidence: 99%

Temperament estimation of toddlers from child–robot interaction with explainable artificial intelligence

et al. 2021

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Personality estimation is a vital ability for communicating with others. It can help robots that interact with humans to model various human behaviors with a few parameters. Numerous studies have proposed models for estimating human personality from human-robot interaction. However, a limited number of methods have focused on the personalities of toddlers, which are dominated by innate temperaments. In this study, we propose a regression model that estimates the toddler temperament from images acquired by a teleoperated childcare robot named ChiCaRo. We gather a dataset from actual interactions between toddlers and ChiCaRo, and extract features from the data to train the regression model. Moreover, an explainable Artificial Intelligence model known as Shapley additive explanations (SHAP) is employed to understand the estimation tendency of the trained model and to compare the tendency with the temperament definition. The proposed model achieved a mean squared error of 0.024 for the average of all temperament factors. The analysis of SHAP confirmed that the model could reasonably learn the tendency compared to the definition in most temperament factors and suggested the possibility of data bias under a specific temperament factor.

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