ROSAME

class amlgym.algorithms.ROSAME.ROSAME(**kwargs)[source]

Bases: PassiveAlgorithmAdapter

Adapter class for running an unofficial implementation of the ROSAME algorithm: “Neuro-Symbolic Learning of Lifted Action Models from Visual Traces”, Kai Xi, Stephen Gould, Sylvie Thiebaux, Proceedings of the Thirty-Fourth International Conference on Automated Planning and Scheduling, 2024. https://ojs.aaai.org/index.php/ICAPS/article/download/31528/33688

Example

from amlgym.algorithms import get_algorithm
rosame = get_algorithm('ROSAME')
model = rosame.learn('path/to/domain.pddl', ['path/to/trace0', 'path/to/trace1'])
print(model)
__init__(**kwargs)[source]
learn(domain_path, trajectory_paths, use_problems=True)[source]
Learns a PDDL action model from:

  1. a (possibly empty) input model which is required to specify the predicates and operators signature;

  2. a list of trajectory file paths.

Parameters:

  • domain_path (str) – input PDDL domain file path

  • trajectory_paths (List[str]) – list of trajectory file paths

  • use_problems (bool) – boolean flag indicating whether to provide the set of objects specified in the problem from which the trajectories have been generated

Return type:

str

Returns:

a string representing the learned PDDL model