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arXiv 2307.06007cs.HCcs.RO
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构建具有社会权力策略的说服性机器人

Building Persuasive Robots with Social Power Strategies

INESC-ID(里斯本系统与计算机工程研究所)

Mojgan Hashemian, Marta Couto, Samuel Mascarenhas, Ana Paiva, Pedro A. Santos, Rui Prada

AI总结:

本研究基于French和Raven的社会权力理论,通过三项用户实验证明社交机器人运用专家、奖励和强制等权力基础能有效增强说服力,且效果持久,为人机交互设计提供依据。

摘要:

社会权力能否赋予社交机器人说服能力?本文介绍了我们近期在设计说服性社交机器人方面的努力。我们设计并开展了三项不同的用户研究,以调查不同社会权力基础(受French和Raven理论启发)对人们遵从社交机器人请求的有效性。结果表明,施加社会权力(特别是来自专家、奖励和强制基础)的机器人说服者展现出更强的影响人类的能力。第一项研究给出了肯定答案,并表明在相同情境下,不同性格的人偏好使用特定社会权力基础的机器人。此外,社会奖励在说服个体方面是有用的。第二项研究表明,通过运用社会权力,社交机器人能够客观地说服人们从多个选项中选出不太理想的一个。最后,第三项研究表明,权力对说服的影响不会随时间衰减,并且在特定情境下可能增强。此外,施加更强的社会权力并不必然导致更高的说服力。总体而言,我们认为这些研究的结果对于设计人机交互场景(尤其是旨在促进行为改变的场景)具有相关性。

英文摘要:

Can social power endow social robots with the capacity to persuade? This paper represents our recent endeavor to design persuasive social robots. We have designed and run three different user studies to investigate the effectiveness of different bases of social power (inspired by French and Raven's theory) on peoples' compliance to the requests of social robots. The results show that robotic persuaders that exert social power (specifically from expert, reward, and coercion bases) demonstrate increased ability to influence humans. The first study provides a positive answer and shows that under the same circumstances, people with different personalities prefer robots using a specific social power base. In addition, social rewards can be useful in persuading individuals. The second study suggests that by employing social power, social robots are capable of persuading people objectively to select a less desirable choice among others. Finally, the third study shows that the effect of power on persuasion does not decay over time and might strengthen under specific circumstances. Moreover, exerting stronger social power does not necessarily lead to higher persuasion. Overall, we argue that the results of these studies are relevant for designing human--robot-interaction scenarios especially the ones aiming at behavioral change.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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