ABSTRACT There is a rising trend in exploring the capability of inverse reinforcement learning (IRL) in high dimensional demonstrations. Our aim is to recognise human intents from video data within an IRL framework. For this, we present a two-layered maximum likelihood IRL model. The usefulness of knowledge representation (KR) schemes and availability of advisors at different layers is exploited through this model. Two main aspects are addressed: a. the importance of having abstract high-level information to the IRL framework in terms of semantic object affordance and b. deductively exploring the utility of a state at different temporal abstractions. The effectiveness of the proposed model has been evaluated with the help of standard Cornell Activity Dataset (CAD-120).

中文翻译：

---

一种用于识别人类意图的知识驱动的分层逆强化学习方法

摘要 在高维演示中探索逆强化学习 (IRL) 的能力呈上升趋势。我们的目标是在 IRL 框架内从视频数据中识别人类意图。为此，我们提出了一个两层最大似然 IRL 模型。通过该模型，可以利用知识表示 (KR) 方案的有用性和不同层顾问的可用性。主要涉及两个方面： a．就语义对象可供性和 b. 而言，具有抽象的高级信息对 IRL 框架的重要性。演绎地探索一个状态在不同时间抽象上的效用。已在标准康奈尔活动数据集 (CAD-120) 的帮助下评估了所提出模型的有效性。