For personal assistive technologies to effectively support users, they need a user model that records information about the user, such as their goals, values, and context. Knowledge-based techniques can model the relationships between these concepts, enabling the support agent to act in accordance with the user's values. However, user models require updating over time to accommodate changes and continuously align with what the user deems important. In our work, we propose and investigate the use of human-agent alignment dialogues for establishing whether user model updates are needed and acquiring the necessary information for these updates. In this paper, we perform an exploratory qualitative focus group study in which we investigate participants' opinions about written examples of alignment dialogues, as a foundation for their design. Transcripts were analyzed using thematic analysis. A main theme that emerged concerns the potential impact of agent utterances on the user's feelings about themselves and about the agent.

In this paper we present a Multimodal Echoborg interface to explore the effect of different embodiments of an Embodied Conversational Agent (ECA) in an interaction. We compared an interaction where the ECA was embodied as a virtual human (VH) with one where it was embodied as an Echoborg, i.e, a person whose actions are covertly controlled by a dialogue system. The Echoborg in our study not only shadowed the speech output of the dialogue system but also its non-verbal actions. The interactions were structured as a debate between three participants on an ethical dilemma. First, we collected a corpus of debate sessions with three humans debaters. This we used as baseline to design and implement our ECAs. For the experiment, we designed two debate conditions. In one the participant interacted with two ECAs both embodied by virtual humans). In the other the participant interacted with one ECA embodied by a VH and the other by an Echoborg. Our results show that a human embodiment of the ECA overall scores better on perceived social attributes of the ECA. In many other respects the Echoborg scores as poorly as the VH except copresence.

An important aspect of human emotion perception is the use of contextual information to understand others' feelings even in situations where their behavior is not very expressive or has an emotionally ambiguous meaning. For technology to successfully detect affect, it must mimic this human ability when analyzing audiovisual input. Databases upon which machine learning algorithms are trained should capture the context of social interactions as well as the behavior expressed in them. However, there is a lack of consensus about what constitutes relevant context in such databases. In this article, we make two contributions towards overcoming this challenge: (a) we identify two principal sources of context for emotion perceptions based on psychological theory, and (b) we provide an overview of how each of these has been considered in published databases covering social interactions. Our results show that a similar set of contextual features are present across the reviewed databases. Between all the different databases researchers seem to have taken into account a set of contextual features reflecting the sources of context seen in psychological theory. However, within individual databases, these features are not yet systematically varied. This is problematic because it prevents them from being used directly as resources for the modeling of context-sensitive affect detection. Based on our findings, we suggest improvements for the future development of affective databases.