Empathic Agent Technology (EAT)

A new view on empathic agents is introduced, named: Empathic Agent Technology (EAT). It incorporates a speech analysis, which provides an indication for the amount of tension present in people. It is founded on an indirect physiological measure for the amount of experienced stress, defined as the variability of the fundamental frequency of the human voice. A thorough review of literature is provided on which the EAT is founded. In addition, the complete processing line of this measure is introduced. Hence, the first generally applicable, completely automated technique is introduced that enables the development of truly empathic agents

An empirical framework for human-robot proxemics

The work described in this paper was conducted within the EU Integrated Projects COGNIRON ("The Cognitive Robot Companion") and LIREC (LIving with Robots and intEractive Companions) and was funded by the European Commission under contract numbers FP6- 002020 and FP7-215554.An empirical framework for Human-Robot (HR) proxemics is proposed which shows how the measurement and control of interpersonal distances between a human and a robot can be potentially used by the robot to interpret, predict and manipulate proxemic behaviour for Human-Robot Interactions (HRIs). The proxemic framework provides for incorporation of inter-factor effects, and can be extended to incorporate new factors, updated values and results. The framework is critically discussed and future work proposed

Chatbots for learning: A review of educational chatbots for the Facebook Messenger

With the exponential growth in the mobile device market over the last decade, chatbots are becoming an increasingly popular option to interact with users, and their popularity and adoption are rapidly spreading. These mobile devices change the way we communicate and allow ever-present learning in various environments. This study examined educational chatbots for Facebook Messenger to support learning. The independent web directory was screened to assess chatbots for this study resulting in the identification of 89 unique chatbots. Each chatbot was classified by language, subject matter and developer's platform. Finally, we evaluated 47 educational chatbots using the Facebook Messenger platform based on the analytic hierarchy process against the quality attributes of teaching, humanity, affect, and accessibility. We found that educational chatbots on the Facebook Messenger platform vary from the basic level of sending personalized messages to recommending learning content. Results show that chatbots which are part of the instant messaging application are still in its early stages to become artificial intelligence teaching assistants. The findings provide tips for teachers to integrate chatbots into classroom practice and advice what types of chatbots they can try out.Web of Science151art. no. 10386