Instance-Specific Remodelling of Planning Domains by Adding Macros and Removing Operators

We propose an approach to remodelling classical planning domains via the addition of macro operators and removal of original operators either for the domain as a whole or instance-by-instance. For the latter remodelling, we train a predictor to choose the best reformulation of the domain based on instance characteristic. In the domain level remodelling, we try find a fixed remodelling that works best on average over our training set. Operator removal does not generally preserve solubility and proving solubility preservation of domain models is PSPACE-complete. So we use an approach that uses training instances to empirically estimate the probability of solubility preservation and maintains a minimum value of that probability on the training instances. We show that the instance-specific approach outperforms the traditional best-on-average macro-only remodelling approach in 9 out of 14 cases of domain/macro-source combinations, and that it can outperform fixed domain-based models generated with existing macro learning tools.