Smart Human, Smarter Robot: How Cheating Affects Perceptions of Social Agency
Smart Human, Smarter Robot: How Cheating Affects Perceptions of Social Agency
复制标题
聪明的人类，更聪明的机器人：作弊如何影响社会机构的看法
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
B. Scassellati
中科院分区：
文献类型：
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作者：
D. Ullman;Iolanda Leite;Jonathan Phillips;Julia Kim;B. Scassellati
《聪明人，更聪明的机器人：欺骗如何影响人们对社会机构的看法》丹尼尔·乌尔曼1，约兰达·莱特2，乔纳森·菲利普斯1，3，4，朱莉娅·金-科恩3和布莱恩·斯卡塞尔拉蒂1，2认知科学项目|2计算机科学系|3心理系|4哲学系，美国康涅狄格州纽黑文耶鲁大学，06520，CT 06520美国耶鲁大学抽象人机交互研究和人与人交互研究经常得到类似的结果。然而，当在机器人中操纵高水平的明显认知线索时，情况并不总是如此。我们调查了在竞争性博弈的背景下，代理人的类型(人类或机器人)和行为类型(诚实或不诚实)在多大程度上影响代理的感知特征和可信性。我们预测，在不诚实操作中的人类和机器人将获得比诚实操作中的人类和机器人更低的可信性归因，并且机器人将被认为比人类整体更不聪明和刻意。正如预测的那样，在不诚实的操作中，人类和机器人得到的可信度归因较低，但令人惊讶的是，机器人被认为比人类更聪明。图1.人工操作的快照。关键词：社会机器人学；可信性；智能；意向性；代理；人-机器人交互导论认识社会代理特征的重要性不仅限于人类，还延伸到其他生物和非生物的社会代理。对一个实体的行为和认知能力的推断极大地影响了智力的归属(比尔，1990)。与人类智力相关的特性可以归因于动画形状(Scholl&Treoulet，2000)、虚拟代理人(Bickmore&Cassell，2001)和社交机器人(Bainbridge，Hart，Kim&SCassellati，2011；Short，Hart，Vu，&SCassellati，2010)。虽然关于人类的智能概念已经被广泛研究，但其他生物被认为是智能的属性仍然不清楚，特别是社交机器人。更好地理解人们如何对机器人进行社会归因不仅将使机器人专家能够设计出具有更好的社会交互能力的机器人，而且还将增加关于社会代理特征的知识库。Short等人之前的研究。(2010)表明，操纵高级行为线索，特别是作弊和不作弊，会导致机器人产生不同的心理状态。研究人员调查了在石头-剪刀-布-布游戏中作弊机器人的心理状态和意图的归因，这是一项高级测试，旨在探索机器人行为的变化如何影响对机器人机构的看法。两种作弊条件下的参与者对互动的评价不如第三种条件下的参与者公平和诚实，如图2所示。机器人操作快照。没有作弊条件。此外，结果表明，作弊条件下的机器人比没有作弊条件下的机器人对心理状态的归因更多。Short等人的工作。(2010)直接推动了本研究。我们试图通过将对人与机器人交互中的代理线索的分析与对人与人交互中的代理线索的可比分析进行基准比较，来进一步这一研究路线。最终，我们的目标是在作弊行为的背景下检查人们对智力和意向性的看法。有许多因素促成了人们对实体是代理的看法。正如班杜拉(2001)所说：“作为代理人，就是要有意识地通过自己的行为使事情发生。”多年来，研究人员已经确定了对代理归属的重要特征，包括意向性(Bandura，2001)和自我推进的、看起来有目的的运动(Premack，1990；Scholl&Tremunlet，2000)。代理的概念超出了人类的范畴；正如高山(2011)所主张的，“无论
Smart Human, Smarter Robot: How Cheating Affects Perceptions of Social Agency Daniel Ullman 1 , Iolanda Leite 2 , Jonathan Phillips 1,3,4 , Julia Kim-Cohen 3 , and Brian Scassellati 1,2 Program in Cognitive Science | 2 Department of Computer Science | 3 Department of Psychology | 4 Department of Philosophy Yale University, New Haven, CT 06520 USA Abstract Human-robot interaction studies and human-human interaction studies often obtain similar findings. When manipulating high-level apparent cognitive cues in robots, however, this is not always the case. We investigated to what extent the type of agent (human or robot) and the type of behavior (honest or dishonest) affected perceived features of agency and trustworthiness in the context of a competitive game. We predicted that the human and robot in the dishonest manipulation would receive lower attributions of trustworthiness than the human and robot in the honest manipulation, and that the robot would be perceived as less intelligent and intentional than the human overall. The human and robot in the dishonest manipulation received lower attributions of trustworthiness as predicted, but, surprisingly, the robot was perceived to be more intelligent than the human. Figure 1. Snapshot of the human manipulation. Keywords: social robotics; trustworthiness; intelligence; intentionality; agency; human-robot interaction Introduction The importance of recognizing social agentic features is not confined to humans, but extends to other living beings and to nonliving social agents. Inferences about the behavior and cognitive capabilities of an entity greatly influence ascriptions of intelligence (Beer, 1990). Human-like properties related to intelligence can be attributed to animated shapes (Scholl & Tremoulet, 2000), virtual agents (Bickmore & Cassell, 2001), and social robots (Bainbridge, Hart, Kim, & Scassellati, 2011; Short, Hart, Vu, & Scassellati, 2010). While the concept of intelligence has been studied extensively with respect to humans, the properties that contribute to perceptions of other animated beings as intelligent, in particular social robots, are still unclear. A better understanding of how people make social attributions to robots will not only allow roboticists to design robots with better social interactive capabilities, but also will add to the knowledge base on features of social agency. Previous research by Short et al. (2010) showed that manipulating high-level behavioral cues, specifically cheating versus not cheating, causes attributions of different mental states to a robot. The researchers investigated attributions of mental state and intentionality to a cheating robot in a game of rock-paper-scissors, a high-level examination that explored how variations in robotic behavior affected perceptions of a robot’s agency. Participants in the two cheat conditions rated the interaction as less fair and honest than those in the third condition, the Figure 2. Snapshot of the robot manipulation. no cheat condition. Furthermore, the results pointed toward greater attributions of mental state to the robot in the cheat conditions than in the no cheat condition. The work by Short et al. (2010) directly motivates the present research. We seek to further this line of research by benchmarking an analysis of agentic cues in a human-robot interaction against a comparable analysis of agentic cues in a human-human interaction. Ultimately, we aim to examine perceptions of intelligence and intentionality in a context of cheating behavior. There are a number of factors that contribute to perceptions of entities as agentic. As stated by Bandura (2001), “To be an agent is to intentionally make things happen by one’s actions.” Researchers over the years have identified features important to ascriptions of agency, including intentionality (Bandura, 2001) and self-propelled, purposeful-looking movement (Premack, 1990; Scholl & Tremoulet, 2000). The concept of agency extends beyond humans; as argued by Takayama (2011), “Regardless of the