AAAI Publications, Tenth Symposium of Abstraction, Reformulation, and Approximation

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Instance-Specific Remodelling of Planning Domains by Adding Macros and Removing Operators
Maher Alhossaini, J. Christopher Beck

Last modified: 2013-06-19

Abstract


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.

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