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.@en

Counsellors at the child helpline offer a confidential environment for children to be heard and empowered. However, training counsellors on handling children’s conversations in text-based chat can be costly and time-consuming. This paper introduces Lilobot, a conversational agent designed for training counsellors of child helplines. The agent’s dialogue is built on the Belief-Desire-Intention (BDI) model, which, in this case, simulates a child victim of school bullying in a text based interaction. Trainees engage with Lilobot in a role-play format, taking on the counsellor’s role. This interactive system helps trainees learn the Five Phase Model, a conversation protocol child’s helplines use. The system also has a trainer interface, where a trainer can oversee and control Lilobot’s interactions, and see a suggested optimal conversational path. The system was built with three main components - a natural language processing model (using Rasa) and the BDI reasoning model and optimal path generation (using Java Spring).@en

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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