Code, benchmarks and experiment data for the SoCS 2020 paper "An Atom-Centric Perspective on Stubborn Sets" - Helmert, Malte; Seipp, Jendrik; Sievers, Silvan; Röger, Gabriele (Zenodo, 2020)
This bundle contains code, data and benchmarks for reproducing all experiments reported in the paper. roeger-et-al-socs2020-fast-downward.tar.gz contains the implementation which is based on Fast Downward 19.12. All Downward Lab scripts used to run the experiments can be found under experiments/efficient-stubborn-sets (2020-04-*, paper-tables.py, plus reports/parsers). roeger-et-al-socs2020-lab.tar.gz contains a copy of Lab 5.5 (https://github.com/aibasel/lab) roeger-et-al-socs2020-benchmarks.tar.gz contains the benchmarks used in the experiments. It consists of IPC benchmarks used in all optimal sequential tracks of IPCs up to 2018. (suite optimal-strips from https://github.com/aibasel/downward-benchmarks) roeger-et-al-socs2020-data.tar.gz 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.
Beyond Distance Estimates: A New Theory of Heuristics for State-Space Search
Keywords: classical planning; partial order reduction; pruning