# Leveraging Planning Landmarks for Hybrid Online Goal Recognition

arXiv: https://arxiv.org/abs/2301.10571 · HTML: https://mlsanity.com/paper/2301.10571/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 25 Jan 2023.

## Builds on (most cited first, 3 of 3)

- [Landmark-Based Approaches for Goal Recognition as Planning](https://mlsanity.com/paper/1904.11739/) (2019)
- [LSTM-Based Goal Recognition in Latent Space](https://mlsanity.com/paper/1808.05249/) (2018)
- [Accounting for Context in Plan Recognition, with Application to Traffic Monitoring](https://mlsanity.com/paper/1302.4980/) (2013)

## Abstract (arXiv)

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.