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Licensed Unlicensed Requires Authentication Published by De Gruyter Oldenbourg April 3, 2021

EVA 2.0: Emotional and rational multimodal argumentation between virtual agents

Niklas Rach ORCID logo, Klaus Weber, Yuchi Yang, Stefan Ultes, Elisabeth André and Wolfgang Minker

Abstract

Persuasive argumentation depends on multiple aspects, which include not only the content of the individual arguments, but also the way they are presented. The presentation of arguments is crucial – in particular in the context of dialogical argumentation. However, the effects of different discussion styles on the listener are hard to isolate in human dialogues. In order to demonstrate and investigate various styles of argumentation, we propose a multi-agent system in which different aspects of persuasion can be modelled and investigated separately. Our system utilizes argument structures extracted from text-based reviews for which a minimal bias of the user can be assumed. The persuasive dialogue is modelled as a dialogue game for argumentation that was motivated by the objective to enable both natural and flexible interactions between the agents. In order to support a comparison of factual against affective persuasion approaches, we implemented two fundamentally different strategies for both agents: The logical policy utilizes deep Reinforcement Learning in a multi-agent setup to optimize the strategy with respect to the game formalism and the available argument. In contrast, the emotional policy selects the next move in compliance with an agent emotion that is adapted to user feedback to persuade on an emotional level. The resulting interaction is presented to the user via virtual avatars and can be rated through an intuitive interface.

ACM CCS:

Funding source: Deutsche Forschungsgemeinschaft

Award Identifier / Grant number: 376696351

Funding statement: This work has been funded by the Deutsche Forschungsgemeinschaft (DFG) within the project “How to Win Arguments – Empowering Virtual Agents to Improve their Persuasiveness”, Grant Number 376696351, as part of the Priority Program “Robust Argumentation Machines (RATIO)” (SPP-1999).

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Received: 2020-11-27
Revised: 2021-02-12
Accepted: 2021-03-15
Published Online: 2021-04-03
Published in Print: 2021-02-23

© 2021 Walter de Gruyter GmbH, Berlin/Boston