Close encounters : spatial distances between people and a robot of mechanistic appearance

Peer reviewe

Researching interactions between humans and machines: methodological challenges

Communication scholars are increasingly concerned with interactions between humans and communicative agents. These agents, however, are considerably different from digital or social media: They are designed and perceived as life-like communication partners (i.e., as “communicative subjects”), which in turn poses distinct challenges for their empirical study. Hence, in this paper, we document, discuss, and evaluate potentials and pitfalls that typically arise for communication scholars when investigating simulated or non-simulated interactions between humans and chatbots, voice assistants, or social robots. In this paper, we focus on experiments (including pre-recorded stimuli, vignettes and the “Wizard of Oz”-technique) and field studies. Overall, this paper aims to provide guidance and support for communication scholars who want to empirically study human-machine communication. To this end, we not only compile potential challenges, but also recommend specific strategies and approaches. In addition, our reflections on current methodological challenges serve as a starting point for discussions in communication science on how meaning-making between humans and machines can be investigated in the best way possible, as illustrated in the concluding section

A prototype for a conversational companion for reminiscing about images

This work was funded by the COMPANIONS project sponsored by the European Commission as part of the Information Society Technologies (IST) programme under EC grant number IST-FP6-034434. Companions demonstrators can be seen at: http://www.dcs.shef.ac.uk/∼roberta/companions/Web/.This paper describes an initial prototype of the Companions project (www.companions-project.org): the Senior Companion (SC), designed to be a platform to display novel approaches to: (1) The use of Information Extraction (IE) techniques to extract the content of incoming dialogue utterances after an ASR phase. (2) The conversion of the input to RDF form to allow the generation of new facts from existing ones, under the control of a Dialogue Manager (DM), that also has access to stored knowledge and knowledge accessed in real time from the web, all in RDF form. (3) A DM expressed as a stack and network virtual machine that models mixed initiative in dialogue control. (4) A tuned dialogue act detector based on corpus evidence. The prototype platform was evaluated, and we describe this; it is also designed to support more extensive forms of emotion detection carried by both speech and lexical content, as well as extended forms of machine learning. We describe preliminary studies and results for these, in particular a novel approach to enabling reinforcement learning for open dialogue systems through the detection of emotion in the speech signal and its deployment as a form of a learned DM, at a higher level than the DM virtual machine and able to direct the SC’s responses to a more emotionally appropriate part of its repertoire. © 2010 Elsevier Ltd. All rights reserved.peer-reviewe

Towards long-term social child-robot interaction: using multi-activity switching to engage young users

Social robots have the potential to provide support in a number of practical domains, such as learning and behaviour change. This potential is particularly relevant for children, who have proven receptive to interactions with social robots. To reach learning and therapeutic goals, a number of issues need to be investigated, notably the design of an effective child-robot interaction (cHRI) to ensure the child remains engaged in the relationship and that educational goals are met. Typically, current cHRI research experiments focus on a single type of interaction activity (e.g. a game). However, these can suffer from a lack of adaptation to the child, or from an increasingly repetitive nature of the activity and interaction. In this paper, we motivate and propose a practicable solution to this issue: an adaptive robot able to switch between multiple activities within single interactions. We describe a system that embodies this idea, and present a case study in which diabetic children collaboratively learn with the robot about various aspects of managing their condition. We demonstrate the ability of our system to induce a varied interaction and show the potential of this approach both as an educational tool and as a research method for long-term cHRI