EVA: Empowering Virtual Agents to Improve their Persuasiveness


Start date: 01.04.2018
Duration: 3 Years
Funded By: DFG (Deutsche Forschungsgemeinschaft)
Local head of project: Prof. Dr. Elisabeth André
Local scientists:  Klaus Weber, M.Sc. M.Sc.

About the project

Whether speakers are perceived as credible depends not only on the contents of their speech, but also to a large extent on how a message is conveyed. Human communication is not only based on speech, but also comprises other channels, such as gestures, postures, facial expressions and gaze, all of which influence the perception of the audience. In the EVA project, we will investigate typical conversational patterns in public debates including the content and structure of arguments, but also how they are communicated to an audience. Moreover, we will include tactical dialogue acts (so called dodge moves), such as a change of topics or subterfuges, that are often used in real debates. To this end, we will simulate argumentation dialogues between humans through embodied conversational agents. As an application domain, we focus on political discourse. Since political debates have an enormous influence on shaping people‘s mind and attitudes, it is of particular importance to provide people with tools that help them explore the argumentation space by presenting arguments in different forms and thus support their decision-making processes. The use of embodied conversational agents is an audio-visual form of presentation that has been less explored in the area of argumentation mining. It allows us to present arguments in a way that is intuitive and reveals the effect of rational and non-rational elements in a debate. The verbal and nonverbal behaviours of the agents will be determined using a combination of a rule-based approach that is informed by theories on argumentation and a data-driven approach that is informed by corpora of multimodal debates between humans. The arguments for the virtual agents will be automatically extracted from an ontology that will be created using argument mining techniques. We will rely on Reinforcement Learning (RL) to optimize the agents‘ argumentation strategies in an interaction with a simulated opponent. The EVA proposal has been prepared within the Priority Programme Robust Argumentation Machines (RATIO). It addresses the following core question of RATIO: How can arguments be presented intuitively to users in order to support decision-making processes? The EVA project has an interdisciplinary character. It will combine research on multimodal behaviour synthesis and analysis, argumentation mining and dialogue management.