“Alexa, Do You Want to Build a Snowman?” Characterizing Playful Requests to Conversational Agents

Shani, Chen; Libov, Alexander; Tolmach, Sofia; Lewin-Eytan, Liane; Maarek, Yoelle; Shahaf, Dafna

doi:10.1145/3491101.3519870

Cited by 11 publications

(11 citation statements)

References 45 publications

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“…By analogy, we believe the ACCs should, first of all, make the experience more enjoyable and convivial. One of the means to obtain this is to introduce the playfulness (Altarriba and humor (Mancini et al, 2017;Shani et al, 2022) to the behavior of the ACCs. In this line, the previous works, e.g., Khot et al (2019) and Weber et al (2021), focus on entertainment and new forms of interactions with human partners at the table.…”

Section: Discussionmentioning

confidence: 99%

Social robots as eating companions

Niewiadomski

Bruijnes

Huisman

et al. 2022

Front. Comput. Sci.

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Previous research shows that eating together (i.e., commensality) impacts food choice, time spent eating, and enjoyment. Conversely, eating alone is considered a possible cause of unhappiness. In this paper, we conceptually explore how interactive technology might allow for the creation of artificial commensal companions: embodied agents providing company to humans during meals (e.g., a person living in isolation due to health reasons). We operationalize this with the design of our commensal companion: a system based on the MyKeepon robot, paired with a Kinect sensor, able to track the human commensal's activity (i.e., food picking and intake) and able to perform predefined nonverbal behavior in response. In this preliminary study with 10 participants, we investigate whether this autonomous social robot-based system can positively establish an interaction that humans perceive and whether it can influence their food choices. In this study, the participants are asked to taste some chocolates with and without the presence of an artificial commensal companion. The participants are made to believe that the study targets the food experience, whilst the presence of a robot is accidental. Next, we analyze their food choices and feedback regarding the role and social presence of the artificial commensal during the task performance. We conclude the paper by discussing the lessons we learned about the first interactions we observed between a human and a social robot in a commensality setting and by proposing future steps and more complex applications for this novel kind of technology.

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Section: Discussionmentioning

confidence: 99%

Social robots as eating companions

Niewiadomski

Bruijnes

Huisman

et al. 2022

Front. Comput. Sci.

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“…We targeted two playful personal opinion question strategies (Would You Rather and Hypothetical questions) because playfulness is considered important when building rapport between conversational agents and users [38] and humor has been shown to increase likeability and trust [30]. Would You Rather questions have also been used in previous therapeutic settings to encourage conversation [14].…”

Section: Related Workmentioning

confidence: 99%

“…The personal opinion questions are split into two different strategies, Would You Rather choices (WYR) and open-ended Hypothetical questions (HYP). We picked these strategies because playfulness and humor are important when building rapport and trust [38,30]. These strategies have also been characterized as good conversation starters [14] and several lists of questions exist across the internet 1 .…”

Section: Experiments With Personal Opinion Questionsmentioning

confidence: 99%

Let's Get Personal: Personal Questions Improve SocialBot Performance in the Alexa Prize

Bowden¹,

Walker²

2023

Preprint

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There has been an increased focus on creating conversational opendomain dialogue systems in the spoken dialogue community. Unlike traditional dialogue systems, these conversational systems cannot assume any specific information need or domain restrictions, i.e., the only inherent goal is to converse with the user on an unknown set of topics. While massive improvements in Natural Language Understanding (NLU) and the growth of available knowledge resources can partially support a robust conversation, these conversations generally lack the rapport between two humans that know each other. We developed a robust open-domain conversational system, Athena, that real Amazon Echo users access and evaluate at scale in the context of the Alexa Prize competition. We experiment with methods intended to increase intimacy between Athena and the user by heuristically developing a rule-based user model that personalizes both the current and subsequent conversations and evaluating specific personal opinion question strategies in A/B studies. Our results show a statistically significant positive impact on perceived conversation quality and length when employing these strategies.

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“…Nowadays, people continuously interact with IDSSs to make decisions about their private and social life [23]. Common examples include interactions with so-called virtual assistants, also called conversational agents (CA) to stay updated on the latest news or weather conditions, to choose what music to listen to, where to go to buy food or to plan and organize appointments [51,3,59]. Usually, when a CA is text-based and interacts with human users via natural conversational language is also called chatbot [61].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Gender and Personality in Human-AI Teaming: The Case of Collaborative Question Answering

Milella,

Natali,

Scantamburlo

et al. 2023

Lecture Notes in Computer Science

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This paper discusses the results of an exploratory study aimed at investigating the impact of conversational agents (CAs) and specifically their agential characteristics on collaborative decision-making processes. The study involved 29 participants divided into 8 small teams engaged in a question-and-answer trivia-style game with the support of a text-based CA, characterized by two independent binary variables: personality (gentle and cooperative vs blunt and uncooperative) and gender (female vs male). A semi-structured group interview was conducted at the end of the experimental sessions to investigate the perceived utility and level of satisfaction with the CAs. Our results show that when users interact with a gentle and cooperative CA, their user satisfaction is higher. Furthermore, female CAs are perceived as more useful and satisfying to interact with than male CAs. We show that group performance improves through interaction with the CAs, confirming that a stereotype favoring the female with a gentle and cooperative personality combination exists in regard to perceived satisfaction, even though this does not lead to greater perceived utility. Our study extends the current debate about the possible correlation between CA characteristics and human acceptance and suggests future research to investigate the role of gender bias and related biases in human-AI teaming.

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“Alexa, Do You Want to Build a Snowman?” Characterizing Playful Requests to Conversational Agents

Cited by 11 publications

References 45 publications

Social robots as eating companions

Social robots as eating companions

Let's Get Personal: Personal Questions Improve SocialBot Performance in the Alexa Prize

The Impact of Gender and Personality in Human-AI Teaming: The Case of Collaborative Question Answering

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