Application of a System-Wide Trust Strategy when Supervising Multiple Autonomous Agents
Application of a System-Wide Trust Strategy when Supervising Multiple Autonomous Agents
复制标题
监督多个自治代理时全系统信任策略的应用
DOI：
10.1177/1541931213601031
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Tyler H. Shaw
中科院分区：
文献类型：
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作者：
James C. Walliser;Ewart J. de Visser;Tyler H. Shaw
当与复杂系统交互时，操作员信任自动化的方式会影响系统性能。最近的研究表明，人们倾向于广泛应用信任，而不是以校准的方式对系统的每个组成部分表现出特定的信任（例如 Keller & Rice，2010）。虽然这种系统范围的信任效应已经在判断仪表等基本情况下建立，但尚未在现实环境中进行研究，例如与多智能体系统中的自主智能体协作。本研究利用多无人机控制模拟，探讨人们如何在监督控制环境中应用对多自主代理的信任。参与者与四架无人机进行互动，这些无人机利用自动目标识别（ATR）系统来识别敌方或友方目标。当其中一个自主代理不准确并且提供了性能信息时，参与者 1) 不太准确，2) 更有可能验证 ATR 的决定，3) 花费更多时间验证图像，4) 认为其他系统不太值得信赖，即使它们 100% 正确。这些发现支持了先前的工作，证明了全系统信任的普遍性，并扩大了应用全系统信任战略的条件。这项工作表明，多代理系统应该提供精心设计的线索和培训，以减轻系统范围的信任效应。
When interacting with complex systems, the manner in which an operator trusts automation influences system performance. Recent studies have demonstrated that people tend to apply trust broadly rather than exhibiting specific trust in each component of the system in a calibrated manner (e.g. Keller & Rice, 2010). While this System–Wide Trust effect has been established for basic situations such as judging gauges, it has not been studied in realistic settings such as collaboration with autonomous agents in a multi-agent system. This study utilized a multiple UAV control simulation, to explore how people apply trust in multi autonomous agents in a supervisory control setting. Participants interacted with four UAVs that utilized automated target recognition (ATR) systems to identify targets as enemy or friendly. When one of the autonomous agents was inaccurate and performance information was provided, participants were 1) less accurate, 2) more likely to verify the ATR’s determination, 3) spent more time verifying images, and 4) rated the other systems as less trustworthy even though they were 100% correct. These findings support previous work that demonstrated the prevalence of system-wide trust and expand the conditions in which system-wide trust strategies are applied. This work suggests that multi-agent systems should provide carefully designed cues and training to mitigate the system-wide trust effect.