Learning Numerical Action Models from Noisy Input Data

TL;DR

This paper introduces PlanMiner-N, an enhanced algorithm that effectively learns numerical action models from noisy input data, significantly improving upon the original PlanMiner's capabilities in noisy environments.

Contribution

The paper proposes new preprocessing and validation methods to enable PlanMiner to learn accurate numerical planning models from noisy data, expanding its robustness.

Findings

PlanMiner-N outperforms PlanMiner with noisy data

Effective noise detection and filtering methods are developed

Improved accuracy of learned models in IPC domains

Abstract

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.