Code and Data for the IJCAI 2021 paper "Learning Generalized Unsolvability Heuristics for Classical Planning"
| Main Authors: | Simon Ståhlberg, Guillem Francès, Jendrik Seipp |
|---|---|
| Format: | info software Journal |
| Terbitan: |
, 2021
|
| Online Access: |
https://zenodo.org/record/4740387 |
Daftar Isi:
- Classifiers The three classifiers in this paper are in the following files. File 'perfect-classifier.tar.gz' contains the perfect classifier. File 'feature-generator-and-safe-classifier.tar.gz' contains the safe classifier. File 'decision-trees.tar.gz' contains the decision-tree classifier. The input to each classifier is a feature file, which is described next. Experiment Data The data used to produce features in this paper is generated in three steps, in the following order. File 'train-test-pddl.tar.gz' contains the PDDL files used to sample states from. File 'train-test-states.tar.gz' contains the states samples from the PDDL files. States are sampled by software in file 'state-space-expansion.tar.gz'. File 'train-features.tar.gz' contains the features generated from the sampled states. Features are generated by software in file 'feature-generator-and-safe-classifier.tar.gz'. Benchmarks The file 'modified-fast-downward.tar.gz' contains a modified version of Fast Downward planning system (https://fast-downward.org). The benchmarks computed with it is in the file 'baselines.tar.gz'.