Abstract
Goal recognition is an important problem in many application domains (e.g., pervasive computing, intrusion detection, computer games, etc.). In many application scenarios it is important that goal recognition algorithms can recognize goals of an observed agent as fast as possible and with minimal domain knowledge. Hence, in this paper, we propose a hybrid method for online goal recognition that combines a symbolic planning landmark based approach and a data-driven goal recognition approach and evaluate it in a real-world cooking scenario. The empirical results show that the proposed method is not only significantly more efficient in terms of computation time than the state-of-the-art but also improves goal recognition performance. Furthermore, we show that the utilized planning landmark based approach, which was so far only evaluated on artificial benchmark domains, achieves also good recognition performance when applied to a real-world cooking scenario.
Abstract (translated)
目标识别在许多应用领域中是一个重要问题(例如,无处不在的计算、入侵检测、计算机游戏等)。在许多应用场景中,重要的是,目标识别算法能够尽可能快地识别观察到的目标,并 minimal domain knowledge。因此,在本文中,我们提出了一种在线目标识别的混合方法,它结合了符号规划地标方法和数据驱动的目标识别方法,并在真实的烹饪场景中进行评估。实验结果显示, proposed 方法不仅在计算时间方面比现有方法更高效,而且改善了目标识别性能。此外,我们表明,目前仅基于人工基准 domains 的目标识别方法,将其应用于真实的烹饪场景也能够实现良好的识别性能。
URL
https://arxiv.org/abs/2301.10571