Passive Algorithms
AMLGym provides seamless integration with several algorithms for passive learning classical planning domains from an input set of trajectories in the following settings: 1. full observability: SAM [1]. 2. partial observability: OffLAM [2]. 3. full and noisy observability: NOLAM [3], ROSAME [4].
[1] [“Safe Learning of Lifted Action Models”, B. Juba and H. S. Le, and R. Stern, Proceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning, 2021.](https://proceedings.kr.org/2021/36/)
[2] [“Lifted Action Models Learning from Partial Traces”, L. Lamanna, L. Serafini, A. Saetti, A. Gerevini, and P. Traverso, Artificial Intelligence Journal, 2025.](https://www.sciencedirect.com/science/article/abs/pii/S0004370224001929)
[3] [“Action Model Learning from Noisy Traces: a Probabilistic Approach”, L. Lamanna and L. Serafini, Proceedings of the Thirty-Fourth International Conference on Automated Planning and Scheduling, 2024.]( https://ojs.aaai.org/index.php/ICAPS/article/view/31493)
[4] [“Neuro-symbolic learning of lifted action models from visual traces”, X. Kai, S. Gould, and S. Thiébaux, Proceedings of the Thirty-Fourth International Conference on Automated Planning and Scheduling, 2024.](https://ojs.aaai.org/index.php/ICAPS/article/download/31528/33688)