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Effect of Agents’ Transparency on Teamwork


Abstract Transparency in the field of human-machine interaction and artificial intelligence has seen a growth of interest in the past few years. Nonetheless, there are still few experimental studies on how transparency affects teamwork, in particular in collaborative situations where the strategies of others, including agents, may seem obscure. We explored this problem using a collaborative game scenario with a mixed human-agent team. We investigated the role of transparency in the agents’ decisions, by having agents that reveal and tell the strategies they adopt in the game, in a manner that makes their decisions transparent to the other team members. The game embraces a social dilemma where a human player can choose to contribute to the goal of the team (cooperate) or act selfishly in the interest of his or her individual goal (defect). We designed a between-subjects experimental study, with different conditions, manipulating the transparency in a team. The results showed an interaction effect between the agents’ strategy and transparency on trust, group identification and human-likeness. Our results suggest that transparency has a positive effect in terms of people’s perception of trust, group identification and human likeness when the agents use a tit-for-tat or a more individualistic strategy. In fact, adding transparent behaviour to an unconditional cooperator negatively affects the measured dimensions.
Year 2019
Keywords Computer Games;Intelligent Virtual Agents;Multi-Agent Societies;
Authors Silvia Tulli, Filipa Correia, Samuel Mascarenhas, Samuel Gomes, Francisco S. Melo, Ana Paiva
Publisher Springer post-proceedings Lecture Notes in Artificial Intelligence (LNAI)
Organization 1st International Workshop on EXplainable TRansparent Autonomous Agents and Multi-Agent Systems at International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019
Month September
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@proceedings { tulli19, abstract = {Transparency in the field of human-machine interaction and artificial intelligence has seen a growth of interest in the past few years. Nonetheless, there are still few experimental studies on how transparency affects teamwork, in particular in collaborative situations where the strategies of others, including agents, may seem obscure. We explored this problem using a collaborative game scenario with a mixed human-agent team. We investigated the role of transparency in the agents’ decisions, by having agents that reveal and tell the strategies they adopt in the game, in a manner that makes their decisions transparent to the other team members. The game embraces a social dilemma where a human player can choose to contribute to the goal of the team (cooperate) or act selfishly in the interest of his or her individual goal (defect). We designed a between-subjects experimental study, with different conditions, manipulating the transparency in a team. The results showed an interaction effect between the agents’ strategy and transparency on trust, group identification and human-likeness. Our results suggest that transparency has a positive effect in terms of people’s perception of trust, group identification and human likeness when the agents use a tit-for-tat or a more individualistic strategy. In fact, adding transparent behaviour to an unconditional cooperator negatively affects the measured dimensions.}, address = {Cham}, booktitle = {Explainable, Transparent Autonomous Agents and Multi-Agent Systems}, editor = {Calvaresi, Davide and Najjar, Amro and Schumacher, Michael and Fr{\"a}mling, Kary}, isbn = {978-3-030-30391-4}, journal = {EXTRAAMAS 2019: Explainable, Transparent Autonomous Agents and Multi-Agent Systems}, keywords = {Computer Games;Intelligent Virtual Agents;Multi-Agent Societies;}, month = {September}, pages = {pp 22-37}, publisher = {Springer post-proceedings Lecture Notes in Artificial Intelligence (LNAI)}, title = {Effect of Agents’ Transparency on Teamwork}, volume = {1st International Workshop on EXplainable TRansparent Autonomous Agents and Multi-Agent Systems at International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019}, year = {2019}, author = {Silvia Tulli and Filipa Correia and Samuel Mascarenhas and Samuel Gomes and Francisco S. Melo and Ana Paiva} }

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