Understanding an agent's goal through its behavior is a common AI problem called Goal Recognition (GR). This task becomes particularly challenging in dynamic environments where goals are numerous and ever-changing. We introduce the General Dynamic Goal Recognition (GDGR) problem, a broader definition of GR aimed at real-time adaptation of GR systems. This paper presents two novel approaches to tackle GDGR: (1) GC-AURA, generalizing to new goals using Model-Free Goal-Conditioned Reinforcement Learning, and (2) Meta-AURA, adapting to novel environments with Meta-Reinforcement Learning. We evaluate these methods across diverse environments, demonstrating their ability to achieve rapid adaptation and high GR accuracy under dynamic and noisy conditions. This work is a significant step forward in enabling GR in dynamic and unpredictable real-world environments.

翻译：通过智能体行为理解其目标是人工智能领域一个常见问题，称为目标识别。在目标数量众多且不断变化的动态环境中，该任务变得尤为困难。我们提出了通用动态目标识别问题，这是对目标识别更广泛的定义，旨在实现目标识别系统的实时适应。本文提出了两种解决通用动态目标识别的新方法：(1) GC-AURA，利用无模型目标条件强化学习实现对新目标的泛化；(2) Meta-AURA，通过元强化学习适应新环境。我们在多种环境中评估了这些方法，证明了它们在动态和噪声条件下能够实现快速适应并达到较高的目标识别准确率。这项研究为推动目标识别在动态且不可预测的真实环境中的应用迈出了重要一步。