Code and experiment data for the IJCAI 2021 paper "On Weak Stubborn Sets in Classical Planning"

This bundle contains code, scripts and benchmarks for reproducing all experiments reported in the paper. It also contains the data generated for the paper.

sievers-wehrle-ijcai2021-fast-downward.zip contains the implementation based on Fast Downward. It also contains the experiment scripts compatible with Lab 6.3 for reproducing all experiments of the paper, under experiments/weak-stubborn-sets. The scripts 2021-05-10-CRC-* contain configurations for running the experiments and the script paper-tables.py gathers the data and produces plots and tables. (Note that some adjustments to the scripts would need to be done because, e.g., the entire tree is not a repository anymore.)

sievers-wehrle-ijcai2021-benchmarks.zip contains the benchmarks. It consists of the STRIPS IPC benchmarks used in all optimal sequential tracks of IPCs up to 2018 (suite optimal_strips from https://github.com/aibasel/downward-benchmarks).

sievers-wehrle-ijcai2021-lab.tar.gz contains a copy of Lab 6.3 (https://github.com/aibasel/lab).

sievers-wehrle-ijcai2021-data.zip contains the experimental data. Directories without the "-eval" ending contain raw data, distributed over a subdirectory for each experiment. Each of these contain a subdirectory tree structure "runs-*" where each planner run has its own directory. For each run, there are symbolic links to the input PDDL files domain.pddl and problem.pddl (can be resolved by putting the benchmarks directory to the right place), the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), the run script "run" used to start the experiment, and a "properties" file that contains data parsed from the log file(s). Directories with the "-eval" ending contain a "properties" file, which contains a JSON directory with combined data of all runs of the corresponding experiment. In essence, the properties file is the union over all properties files generated for each individual planner run.

Note on license: we chose GPL v3.0 or later mainly because we consider our implementation based on Fast Downward the main contribution of this package, and Fast Downward comes with GPL v3.0. We only include a copy of Lab and the benchmarks for convenience.