Interactive Social Agents from Deep Data

This work was presented at HAIDM 2014. The 2014 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: The multidisciplinary challenge of modelling agents have been driven by theory explaining social phenomena. Yet, these generic models lack of expressiveness. For that reason, data-driven approaches to the design of agents have been pursued, mainly for modelling non-verbal behaviour. In this paper we argue that real data is not only useful for that modality, but it can also assist agent’s design in different phases of the process at different levels of granularity. Furthermore, deep data, which inform us about user’s perception, emotions and motivations is valuable to build fluid interactions with virtual humans. We illustrate our stance with two case studies where we study interpersonal conflict. One study describes the design of agents to populate a serious game aimed at teaching conflict resolution skills to children and the other describes an experiment designed to extract deep data from a dyadic interaction prone to conflict emergence.

Keywords: Virtual Agents, Design, Interpersonal Conflict, Data-driven.

Citation: Joana Campos and Ana Paiva. Interactive Social Agents from Deep Data.

Download: http://bit.ly/1STrWhr

About P. Andreadis

Pre-Doctoral Research Assistant in AI and Social Computation @ University of Edinburgh.

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