Learning Numerical Action Models from Noisy Input Data
  This paper presents the PlanMiner-N algorithm, a domain learning technique
based on the PlanMiner domain learning algorithm. The algorithm presented here
improves the learning capabilities of PlanMiner when using noisy data as input.
The PlanMiner algorithm is able to infer arithmetic and logical expressions to
learn numerical planning domains from the input data, but it was designed to
work under situations of incompleteness making it unreliable when facing noisy
input data. In this paper, we propose a series of enhancements to the learning
process of PlanMiner to expand its capabilities to learn from noisy data. These
methods preprocess the input data by detecting noise and filtering it and study
the learned action models learned to find erroneous preconditions/effects in
them. The methods proposed in this paper were tested using a set of domains
from the International Planning Competition (IPC). The results obtained
indicate that PlanMiner-N improves the performance of PlanMiner greatly when
facing noisy input data.
