totSAT – Totally-Ordered Hierarchical Planning Through SAT
Authors: Gregor Behnke, Daniel Höller, Susanne Biundo
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Furthermore, we have conducted an extensive empirical evaluation to compare our new planner against state-of-the-art HTN planners. It shows that our technique outperforms any of these systems. |
| Researcher Affiliation | Academia | Gregor Behnke, Daniel H oller, Susanne Biundo Institute of Artificial Intelligence, Ulm University, D-89069 Ulm, Germany {gregor.behnke, daniel.hoeller, susanne.biundo}@uni-ulm.de |
| Pseudocode | No | The paper describes algorithms and formulae but does not provide structured pseudocode blocks or algorithms labeled as such. |
| Open Source Code | Yes | Our implementation of tot SAT uses the parser and preprocessor of the planning system PANDA (Bercher, Keen, and Biundo 2014). We will release the code of tot SAT publicly. |
| Open Datasets | No | The paper refers to |
| Dataset Splits | No | The paper mentions using domains and instances (e.g., UM-Translog, Woodworking, Satellite, Smart Phone, ENTERTAINMENT, ROVER, TRANSPORT) for evaluation but does not specify training, validation, or test dataset splits. It describes how instances were created or adapted, but not how they were partitioned for evaluation. |
| Hardware Specification | Yes | Each planner was given 10 minutes runtime and 4 GB of RAM per instance on an Intel Xeon E5-2660. |
| Software Dependencies | No | The paper mentions several software tools and systems (e.g., PANDA, SHOP, HTN2STRIPS, jasper, cryptominisat5, Maple COMSPS, Riss6, minisat) but does not consistently provide specific version numbers for these dependencies to ensure reproducibility. |
| Experiment Setup | No | The paper mentions general experimental conditions like runtime and RAM limits ("10 minutes runtime and 4 GB of RAM per instance") and discusses the timeout setting for the SAT solver ("always set the timeout of the solver to the remaining runtime"). However, it does not provide specific hyperparameters like learning rates, batch sizes, or optimizer settings, which are typically found in experimental setup sections. |