{"title":"论外部代理意图预测器对人机协调的效用","authors":"Chenxu Wang, Zilong Chen, Huaping Liu","doi":"10.5555/3635637.3663222","DOIUrl":null,"url":null,"abstract":"Reaching a consensus on the team plans is vital to human-AI coordination. Although previous studies provide approaches through communications in various ways, it could still be hard to coordinate when the AI has no explainable plan to communicate. To cover this gap, we suggest incorporating external models to assist humans in understanding the intentions of AI agents. In this paper, we propose a two-stage paradigm that first trains a Theory of Mind (ToM) model from collected offline trajectories of the target agent, and utilizes the model in the process of human-AI collaboration by real-timely displaying the future action predictions of the target agent. Such a paradigm leaves the AI agent as a black box and thus is available for improving any agents. To test our paradigm, we further implement a transformer-based predictor as the ToM model and develop an extended online human-AI collaboration platform for experiments. The comprehensive experimental results verify that human-AI teams can achieve better performance with the help of our model. A user assessment attached to the experiment further demonstrates that our paradigm can significantly enhance the situational awareness of humans. Our study presents the potential to augment the ability of humans via external assistance in human-AI collaboration, which may further inspire future research.","PeriodicalId":326727,"journal":{"name":"Adaptive Agents and Multi-Agent Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"On the Utility of External Agent Intention Predictor for Human-AI Coordination\",\"authors\":\"Chenxu Wang, Zilong Chen, Huaping Liu\",\"doi\":\"10.5555/3635637.3663222\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Reaching a consensus on the team plans is vital to human-AI coordination. Although previous studies provide approaches through communications in various ways, it could still be hard to coordinate when the AI has no explainable plan to communicate. To cover this gap, we suggest incorporating external models to assist humans in understanding the intentions of AI agents. In this paper, we propose a two-stage paradigm that first trains a Theory of Mind (ToM) model from collected offline trajectories of the target agent, and utilizes the model in the process of human-AI collaboration by real-timely displaying the future action predictions of the target agent. Such a paradigm leaves the AI agent as a black box and thus is available for improving any agents. To test our paradigm, we further implement a transformer-based predictor as the ToM model and develop an extended online human-AI collaboration platform for experiments. The comprehensive experimental results verify that human-AI teams can achieve better performance with the help of our model. A user assessment attached to the experiment further demonstrates that our paradigm can significantly enhance the situational awareness of humans. Our study presents the potential to augment the ability of humans via external assistance in human-AI collaboration, which may further inspire future research.\",\"PeriodicalId\":326727,\"journal\":{\"name\":\"Adaptive Agents and Multi-Agent Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Adaptive Agents and Multi-Agent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5555/3635637.3663222\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Adaptive Agents and Multi-Agent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5555/3635637.3663222","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
就团队计划达成共识对于人类与人工智能的协调至关重要。虽然以往的研究提供了通过各种方式进行交流的方法,但当人工智能没有可解释的交流计划时,仍然很难进行协调。为了弥补这一缺陷,我们建议结合外部模型来帮助人类理解人工智能代理的意图。在本文中,我们提出了一个两阶段范式,首先从收集到的目标代理离线轨迹中训练出一个心智理论(ToM)模型,然后通过实时显示目标代理的未来行动预测,在人机协作过程中利用该模型。这种范式将人工智能代理作为一个黑盒,因此可用于改进任何代理。为了检验我们的范式,我们进一步实现了一个基于变压器的预测器作为 ToM 模型,并开发了一个扩展的在线人机协作平台进行实验。综合实验结果证实,在我们的模型帮助下,人类-人工智能团队可以取得更好的成绩。实验中附带的用户评估进一步证明,我们的范例可以显著增强人类的态势感知能力。我们的研究展示了在人类-人工智能协作中通过外部援助增强人类能力的潜力,这可能会进一步启发未来的研究。
On the Utility of External Agent Intention Predictor for Human-AI Coordination
Reaching a consensus on the team plans is vital to human-AI coordination. Although previous studies provide approaches through communications in various ways, it could still be hard to coordinate when the AI has no explainable plan to communicate. To cover this gap, we suggest incorporating external models to assist humans in understanding the intentions of AI agents. In this paper, we propose a two-stage paradigm that first trains a Theory of Mind (ToM) model from collected offline trajectories of the target agent, and utilizes the model in the process of human-AI collaboration by real-timely displaying the future action predictions of the target agent. Such a paradigm leaves the AI agent as a black box and thus is available for improving any agents. To test our paradigm, we further implement a transformer-based predictor as the ToM model and develop an extended online human-AI collaboration platform for experiments. The comprehensive experimental results verify that human-AI teams can achieve better performance with the help of our model. A user assessment attached to the experiment further demonstrates that our paradigm can significantly enhance the situational awareness of humans. Our study presents the potential to augment the ability of humans via external assistance in human-AI collaboration, which may further inspire future research.