Leveraging Planning Landmarks for Hybrid Online Goal Recognition
This work addresses the need for efficient and accurate goal recognition in domains like pervasive computing and computer games, representing an incremental improvement over existing methods.
The paper tackles the problem of online goal recognition by proposing a hybrid method combining symbolic planning landmarks and data-driven approaches, achieving significantly faster computation times and improved recognition performance in a real-world cooking scenario.
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.