让智能体在观察者部分可见时，主动优化信息传递策略。
Observer-Aware Probabilistic Planning Under Partial Observability
- 基于观测者感知的马尔可夫决策过程，扩展处理部分可观测场景。
- 支持动态隐藏目标变量，可应对目标变化或可预测性需求。
- 适用于需解释性、可读性的智能体决策系统，如人机协作。
本文研究智能体在知晓存在观测者且观测者处于部分可观测状态下的规划问题。智能体需选择策略以优化观测所传递的信息。基于观测者感知马尔可夫决策过程（OAMDP），我们提出一个新框架，形式化可读性、可解释性和可预测性等性质。该扩展将OAMDP推广至部分可观测情形，不仅处理更真实的场景，还可建模动态隐藏目标变量。这些变量可用于建模目标变化或可预测性任务。我们分析了PO-OAMDP的理论性质，并在基准问题上测试了HSVI算法在特定初始化下的收敛行为及生成策略。
原文摘要 · Abstract (English)
In this article, we are interested in planning problems where the agent is aware of the presence of an observer, and where this observer is in a partial observability situation. The agent has to choose its strategy so as to optimize the information transmitted by observations. Building on observer-aware Markov decision processes (OAMDPs), we propose a framework to handle this type of problems and thus formalize properties such as legibility, explicability and predictability. This extension of OAMDPs to partial observability can not only handle more realistic problems, but also permits considering dynamic hidden variables of interest. These dynamic target variables allow, for instance, working with predictability, or with legibility problems where the goal might change during execution. We discuss theoretical properties of PO-OAMDPs and, experimenting with benchmark problems, we analyze HSVI's convergence behavior with dedicated initializations and study the resulting strategies.
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