In this work, we develop a game-theoretic modeling of the interaction between a human operator and an autonomous decision aid when they collaborate in a multi-agent task allocation setting. In this setting, we propose a decision aid that is designed to calibrate the operator's reliance on the aid through a sequence of interactions to improve overall human-autonomy team performance. The autonomous decision aid employs a long short-term memory (LSTM) neural network for human action prediction and a Bayesian parameter filtering method to improve future interactions, resulting in an aid that can adapt to the dynamics of human reliance. The proposed method is then tested against a large set of simulated human operators from the choice prediction competition (CPC18) data set, and shown to significantly improve human-autonomy interactions when compared to a myopic decision aid that only suggests predicted human actions without an understanding of reliance.
翻译:在这项工作中,我们开发了人类操作者与自主决策援助在多试剂任务分配环境中进行合作时相互作用的游戏理论模型。在这个环境中,我们提议了一种决策援助,旨在通过一系列互动来调整操作者对援助的依赖,以提高人类自主团队的整体性能。自主决策援助使用长期短期记忆(LSTM)神经网络用于人类行动预测,以及一种贝叶斯参数过滤方法来改进未来的相互作用,从而产生一种能够适应人类依赖动态的援助。然后,根据选择预测竞赛(CPC18数据集)的大批模拟人类操作者测试了拟议方法,并表明,与仅暗示不理解依赖的预测人类行动的近视决策援助相比,将显著改善人类自主互动。