基于心智理论建模与可解释强化学习的个性化决策支持
Personalized Decision Supports based on Theory of Mind Modeling and Explainable Reinforcement Learning
AI总结：
本研究提出结合心智理论建模与可解释强化学习的个性化决策支持系统，通过DRL提供建议、ToM预测用户行为以把握干预时机，并用反事实解释增强可解释性，在模拟团队决策众包实验中表现优于基线，且具备广泛应用潜力。
摘要：
本文提出了一种结合心智理论（Theory of Mind, ToM）建模与可解释强化学习（explainable Reinforcement Learning, XRL）的新型个性化决策支持系统，可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）给出专家级动作建议，同时引入ToM建模理解用户心理状态并预测其未来动作，以选择合适的干预时机。为解释干预行为，我们基于强化学习（RL）的特征重要性与用户ToM模型结构，采用反事实解释方法。所提系统能生成准确的个性化干预，且易于最终用户理解。我们通过在模拟团队决策任务中开展的一系列众包实验验证了方法的有效性，结果显示该系统在任务表现上优于控制基线。该方法不依赖特定任务环境与RL模型结构，因此有潜力推广至广泛的应用场景。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
