Learning Numerical Action Models from Noisy Input Data
This work addresses a specific bottleneck in domain learning for planning systems, making it incremental by enhancing an existing algorithm to handle noise.
The paper tackles the problem of learning numerical action models from noisy input data by proposing PlanMiner-N, an enhanced version of PlanMiner, which improves performance significantly in noisy conditions as tested on International Planning Competition domains.
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.