NOLAM

class amlgym.algorithms.NOLAM.NOLAM(noise=0.0)[source]

Bases: PassiveAlgorithmAdapter

Adapter class for running the NOLAM algorithm: “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

Parameters:

noise (float) – The observation noise.

Example

from amlgym.algorithms import get_algorithm
nolam = get_algorithm('NOLAM')
model = nolam.learn('path/to/domain.pddl', ['path/to/trace0', 'path/to/trace1'])
print(model)
__init__(noise=0.0)
learn(domain_path, trajectory_paths)[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

Return type:

str

Returns:

a string representing the learned PDDL model

noise: float = 0.0