Code, benchmarks and experiment data for the IPC 2018 entry "Delfi: Online Planner Selection for Cost-Optimal 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.

katz-et-al-icp2018-delfi.zip and katz-et-al-icp2018-delf2.zip contain the implementation based on Fast Downward (the two folders only differ in how the planner selection works). It also contains the experiment scripts (hopefully) compatible with Lab 2.1 for reproducing all experiments of the paper, under experiments/ipc2018.

katz-et-al-ipc2018-scripts.zip contain all scripts for the learning pipeline used to train the planner selection models.

katz-et-al-icp2018-benchmarks.zip contains the benchmarks. It consists of the IPC benchmarks used in all optimal sequential tracks of IPCs up to 2014 (suite optimal from https://github.com/aibasel/downward-benchmarks).

katz-et-al-icp2018-lab.zip contains a copy of Lab 2.1 (https://github.com/aibasel/lab).

katz-et-al-icp2018-raw-data.zip and katz-et-al-icp2018-parsed-data.zip contain the experimental data. Directories in katz-et-al-icp2018-raw-data.zip (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 in katz-et-al-icp2018-parsed-data.zip (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.

The image data set used for training can be found online: https://github.com/IBM/IPC-image-data

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.