This repository serves as a documentation for the experiments described in the Master's Thesis "Pruning Techniques for Lifted SAT-Based Hierarchical Planning" (https://github.com/NikolaiLMS/lilotane).
The softwares we evaluated are located here:
- Our modified Lilotane: https://github.com/NikolaiLMS/lilotane (forked from https://github.com/domschrei/lilotane)
- PandaPIParser: https://github.com/panda-planner-dev/pandaPIparser
- PandaPIGrounder: https://github.com/panda-planner-dev/pandaPIgrounder
- PandaPIEngine: https://github.com/panda-planner-dev/pandaPIengine
- HyperTensioN: https://github.com/Maumagnaguagno/HyperTensioN and https://gitlab.anu.edu.au/u1092535/ipc2020-competitor-4
Additionally we used the following software to help with test execution:
- Runwatch: https://github.com/domschrei/runwatch
- Hypertension was executed with the commands shown in runwatch_commands.txt, we did not use the the IPC optimizations as was later discovered and described in #1
- PandaGBFS was executed with the commands shown in runwatch_commands.txt and the execute_pandagbfs.sh script
- PandaSAT was executed with the commands shown in runwatch_commands.txt and the execute_pandasat.sh script
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instances/
contains our training and test data, which consists of the total-order instances of the IPC 2020 (https://github.com/panda-planner-dev/ipc2020-domains/tree/master/total-order) -
evaluations/
contains the experimental data for the Parameter Evaluation, the Per Domain Evaluation and State of The Art Evaluation sections of the thesis, consisting of the logs for each instance, aswell as additional files that contain already computed metrics like IPC-Score, PAR2-Score, Coverage etc. -
figures/
contains our created plots using the evaluation data -
util/
contains multiple scripts we created to aid our experiments