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.

本文提出了一种在线目标识别混合方法，结合了符号规划和数据驱动方法， 并在真实世界的烹饪场景中进行了评估，研究结果表明，与现有技术相比该方法在计算时间上更高效，同时也提高了目标识别的性能。另外，论文也证明了该方法适用于真实世界的场景。