In this paper, we propose a novel personalized decision support system that combines Theory of Mind (ToM) modeling and explainable Reinforcement Learning (XRL) to provide effective and interpretable interventions. Our method leverages DRL to provide expert action recommendations while incorporating ToM modeling to understand users' mental states and predict their future actions, enabling appropriate timing for intervention. To explain interventions, we use counterfactual explanations based on RL's feature importance and users' ToM model structure. Our proposed system generates accurate and personalized interventions that are easily interpretable by end-users. We demonstrate the effectiveness of our approach through a series of crowd-sourcing experiments in a simulated team decision-making task, where our system outperforms control baselines in terms of task performance. Our proposed approach is agnostic to task environment and RL model structure, therefore has the potential to be generalized to a wide range of applications.

翻译：本文提出一种结合心智理论（Theory of Mind, ToM）建模与可解释强化学习（Explainable Reinforcement Learning, XRL）的个性化决策支持系统，旨在提供有效且可解释的干预措施。该方法利用深度强化学习（DRL）提供专家级行动建议，同时通过ToM建模理解用户心理状态并预测其未来行为，从而确定干预的恰当时机。为解释干预逻辑，我们基于强化学习的特征重要性与用户ToM模型结构生成反事实解释。所提出的系统能够生成准确且个性化的干预方案，且对终端用户具有高度可解释性。通过在一项模拟团队决策任务中开展系列众包实验，我们验证了该方法的有效性——与对照组基线相比，我们的系统在任务性能上表现更优。该方法对任务环境与强化学习模型结构具有通用性，因此具备推广至广泛应用的潜力。