Marhaba, how may i help you?

Salem, Maha; Ziadee, Micheline; Sakr, Majd

doi:10.1145/2559636.2559683

Cited by 55 publications

(6 citation statements)

References 15 publications

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“…It would also promote inter-and cross-cultural research, as well as inter-and cross-ASA research. Both are relevant research goals for the ASA community, which is fundamentally interested in variation between two populations: humans and ASAs; for example, how different cultural groups perceived specific ASAs (Qu et al, 2013;Salem et al, 2014;Obaid et al, 2016). We also hope that the work and procedure presented to provide a blueprint for future translations of ASA questionnaires into other languages.…”

Section: Discussionmentioning

confidence: 99%

Mandarin Chinese translation of the Artificial-Social-Agent questionnaire instrument for evaluating human-agent interaction

Li,

Fitrianie,

Bruijnes

et al. 2023

Front. Comput. Sci.

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The Artificial-Social-Agent (ASA) questionnaire is an instrument for evaluating human-ASA interaction. It consists of 19 constructs and related dimensions measured by either 24 questionnaire items (short version) or 90 questionnaire items (long version). The questionnaire was built and validated by a research community effort to make evaluation results more comparable between agents and findings more generalizable. The current questionnaire is in English, which limits its use to only a population with an adequate command of the English language. Translating the questionnaire into more languages allows for the inclusion of other populations and the possibility of comparing them. Therefore, this paper presents a Mandarin Chinese translation of the questionnaire. After three construction cycles that included forward and backward translation, we gave both the final version of the translated and original English questionnaire to 242 bilingual crowd-workers to evaluate 14 ASAs. Results show on average a good level of correlation on the construct/dimension level (ICC M = 0.79, SD = 0.09, range [0.61, 0.95]) and on the item level (ICC M = 0.62, SD = 0.14, range [0.19, 0.92]) between the two languages for the long version, and for the short version (ICC M = 0.66, SD = 0.12, range [0.41, 0.92]). The analysis also established correction values for converting questionnaire item scores between Chinese and English questionnaires. Moreover, we also found systematic differences in English questionnaire scores between the bilingual sample and a previously collected mixed-international English-speaking sample. We hope this and the Chinese questionnaire translation will motivate researchers to study human-ASA interaction among a Chinese literate population and to study cultural similarities and differences in this area.

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Section: Discussionmentioning

confidence: 99%

Mandarin Chinese translation of the Artificial-Social-Agent questionnaire instrument for evaluating human-agent interaction

Li,

Fitrianie,

Bruijnes

et al. 2023

Front. Comput. Sci.

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“…In-person versus online reactions are also likely to be different, so future studies with online failure detection are needed to validate the transferability of the model. Finally, we note the need to account for demographic and cultural factors which can also play a role on the reactions elicited and performance of the system [34,35]. This work contributes with the BAD dataset and BADNet, important tools to advance the field of human social cue recognition for failure detection, that extend on existing work by including a wide variety of failure types and collecting bystander reactions in uncontrolled environments, through an online data collection method.…”

Section: Future Workmentioning

confidence: 95%

The Bystander Affect Detection (BAD) Dataset for Failure Detection in HRI

Bremers¹,

Parreira²,

Fang³

et al. 2023

Preprint

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For a robot to repair its own error, it must first know it has made a mistake. One way that people detect errors is from the implicit reactions from bystanders -their confusion, smirks, or giggles clue us in that something unexpected occurred. To enable robots to detect and act on bystander responses to task failures, we developed a novel method to elicit bystander responses to human and robot errors. Using 46 different stimulus videos featuring a variety of human and machine task failures, we collected a total of 2452 webcam videos of human reactions from 54 participants. To test the viability of the collected data, we used the bystander reaction dataset as input to a deep-learning model, BADNet, to predict failure occurrence. We tested different data labeling methods and learned how they affect model performance, achieving precisions above 90%. We discuss strategies to model bystander reactions and predict failure and how this approach can be used in real-world robotic deployments to detect errors and improve robot performance. As part of this work, we also contribute with the "Bystander Affect Detection" (BAD) dataset of bystander reactions, supporting the development of better prediction models.

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“…Design approachable robots that provide people with advice, feedback and instructions in a manner that encourages cooperation and compliance, particularly during first encounters (e.g., by adhering to social and cultural norms). Yamamoto et al (1992), Torrey (2009), Wang et al (2010), Torrey et al (2013), Trovato et al (2013, Salem et al (2014), Geiskkovitch et al (2016, Gehle et al (2017), Choi et al (2019), Xu (2019), Hashemian et al (2019), Sanoubari et al (2019), Lim et al (2020), Avelino et al (2021), Ullrich et al (2021), Fischer et al (2021, Tian and Oviatt (2021) Description: Units should be actively encouraged to take initiative in helping other units resolve problems. Motivating mechanisms and incentives must be formed.…”

Section: Preparing For Unexpected Robot Failures That Challenge the Ecosystemmentioning

confidence: 99%

Expect the Unexpected: Leveraging the Human-Robot Ecosystem to Handle Unexpected Robot Failures

Honig

Oron-Gilad

2021

Front. Robot. AI

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Unexpected robot failures are inevitable. We propose to leverage socio-technical relations within the human-robot ecosystem to support adaptable strategies for handling unexpected failures. The Theory of Graceful Extensibility is used to understand how characteristics of the ecosystem can influence its ability to respond to unexpected events. By expanding our perspective from Human-Robot Interaction to the Human-Robot Ecosystem, adaptable failure-handling strategies are identified, alongside technical, social and organizational arrangements that are needed to support them. We argue that robotics and HRI communities should pursue more holistic approaches to failure-handling, recognizing the need to embrace the unexpected and consider socio-technical relations within the human robot ecosystem when designing failure-handling strategies.

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Marhaba, how may i help you?

Cited by 55 publications

References 15 publications

Mandarin Chinese translation of the Artificial-Social-Agent questionnaire instrument for evaluating human-agent interaction

Mandarin Chinese translation of the Artificial-Social-Agent questionnaire instrument for evaluating human-agent interaction

The Bystander Affect Detection (BAD) Dataset for Failure Detection in HRI

Expect the Unexpected: Leveraging the Human-Robot Ecosystem to Handle Unexpected Robot Failures

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