Abstract
With the development of intelligent technology, autonomous agents are no longer just simple tools; they have gradually become our partners. This paper presents a trust-based human-autonomous teaming (HAT) framework to realize tactical coordination between human and autonomous agents. The proposed trust-based HAT framework consists of human and autonomous trust models, which leverage a fusion mechanism to fuse multiple performance metrics to generate trust values in real-time. To obtain adaptive trust models for a particular task, a reinforcement learning algorithm is used to learn the fusion weights of each performance metric from human and autonomous agents. The adaptive trust models enable the proposed trust-based HAT framework to coordinate actions or decisions of human and autonomous agents based on their trust values. We used a ball-collection task to demonstrate the coordination ability of the proposed framework. Our experimental results show that the proposed framework can improve work efficiency.
This work was supported in part by the Australian Research Council (ARC) under discovery grant DP210101093 and DP220100803. Research was also sponsored in part by the Australia Defence Innovation Hub under Contract No. P18-650825, US Office of Naval Research Global under Cooperative Agreement Number ONRG - NICOP - N62909- 19-1-2058, and AFOSR - DST Australian Autonomy Initiative agreement ID10134. We also thank the NSW Defence Innovation Network and NSW State Government of Australia for financial support in part of this research through grant DINPP2019 S1-03/09 and PP21-22.03.02.
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Ma, W., Chang, YC., Wang, YK., Lin, CT. (2022). Human-Autonomous Teaming Framework Based on Trust Modelling. In: Aziz, H., Corrêa, D., French, T. (eds) AI 2022: Advances in Artificial Intelligence. AI 2022. Lecture Notes in Computer Science(), vol 13728. Springer, Cham. https://doi.org/10.1007/978-3-031-22695-3_49
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