D 2022

Upward Influence Tactics: Playful Virtual Reality Approach for Analysing Human Multi-robot Interaction

GERDENITSCH, Cornelia; Matthias WEINHOFER; Jaison PUTHENKALAM a Simone KRIGLSTEIN

Základní údaje

Originální název

Upward Influence Tactics: Playful Virtual Reality Approach for Analysing Human Multi-robot Interaction

Autoři

GERDENITSCH, Cornelia; Matthias WEINHOFER; Jaison PUTHENKALAM a Simone KRIGLSTEIN

Vydání

Bremen, Germany, Entertainment Computing – ICEC 2022: 21st IFIP TC 14 International Conference, od s. 76-88, 13 s. 2022

Nakladatel

Springer

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Švýcarsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Odkazy

Impakt faktor

Impact factor: 0.402 v roce 2005

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14330/22:00127295

Organizační jednotka

Fakulta informatiky

ISBN

978-3-031-20211-7

ISSN

EID Scopus

Klíčová slova anglicky

Virtual reality; Robot; Leadership; Influence tactics; Human robot interaction

Příznaky

Mezinárodní význam, Recenzováno
Změněno: 13. 8. 2025 17:45, Mgr. Petra Trembecká, Ph.D.

Anotace

V originále

The interest, the potential, and also the technical development in artificial intelligence assistants shows us that these will play an essential role in the future of work. Exploring the interaction and communication between human and artificial intelligence (AI) assistants forms the basis for the development of trustworthy and meaningful AI-based systems. In this paper we focused on the question how humans react to AI - more precisely, AI gents as robots - that act to influence human behavior and emotions by using two upward influencing tactics: Ingratiating and Blocking. For this purpose, we developed a playful virtual reality approach that creates a leader-subordinate relationship between humans and the AI agents in a factory environment. We explore how humans react to those agents. Among other things, we found that behaviors that are seen as likable in humans are perceived as distracting in robots (e.g., compliments used by the ingratiating tactic). Further, robots were perceived as a group and not as individuals. Our findings showed us directions and open questions which need to be investigated in future work investigating human-multi-robot interaction at the workplace.