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首页> 外文期刊>International journal of human-computer studies >Understanding human-robot teams in light of all-human teams: Aspects of team interaction and shared cognition
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Understanding human-robot teams in light of all-human teams: Aspects of team interaction and shared cognition

机译：了解人类机器人借鉴了人类团队：团队互动和共同认知的方面

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As robots become more autonomous, their roles shift from being operated and controlled by humans to interactively teaming with humans. The current research focuses on how human operators can effectively team with autonomous urban search and rescue agents in a dynamic and complex task environment. To do so, we empirically examined how shared cognition and restricted language capabilities impacted performance of human-robot dyad search teams using a simulated Minecraft task environment. In order to examine the effects of shared mental models and language the following modified conditions were applied: (1) participants were either able to communicate using natural language or the internal participant's communication was limited to three-word utterances; and (2) shared mental models were manipulated by either the internal participant being made fully aware of the external participant's restricted representation of the environment and inaccurate map or the internal was unaware of these challenges. The primary findings from this study are: (1) teams in the natural language and shared mental model conditions performed better than teams in the limited language and restricted model conditions; (2) when the internal participant was unaware of the challenges of the external, the external perceived higher workload than when there was a shared mental model; (3) teams with natural language and shared mental model demonstrated more predictable behavior than the other teams; (4) some amount of systems' predictability was good but too much predictability was not good. Overall, these results indicate that effective team interaction and shared cognition play an important role in human-robot dyadic teaming performance.
机译：随着机器人变得更加自主的，他们的角色从人类运营和控制的转变，以与人类交互式联系。目前的研究侧重于人类运营商如何在动态和复杂的任务环境中有效地与自主城市搜索和救援代理一起团队。为此，我们经验检查了使用模拟的MINECRAFT任务环境的人机Dyad搜索团队的分享了认知和限制语言能力。为了检查共享心理模型和语言的影响，应用了以下修改条件：（1）参与者可以使用自然语言进行沟通，或者内部参与者的沟通仅限于三字话题; （2）共享心理模型由内部参与者完全了解外部参与者的环境限制性的环境和不准确的地图或内部不知道这些挑战。本研究的主要发现包括：（1）自然语言的团队和共享心理模型条件比在有限的语言和限制模型条件下的团队更好; （2）当内部参与者不知道外部的挑战时，外部感知更高的工作量比共同心理模型更高的工作量; （3）具有自然语言和共享心理模型的团队表现出比其他团队更具可预测的行为; （4）一些系统的可预测性好，但太多的可预测性并不好。总体而言，这些结果表明，有效的团队互动和共同认知在人机二级组织绩效中发挥着重要作用。

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