Graph-Based Factorization of Classical Planning Problems

Authors: Martin Wehrle, Silvan Sievers, Malte Helmert

IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental As a proof of concept, we slightly modified the IPC Visitall domain such that moving and marking as visited are represented by different operators. We applied the Fast Downward planner [Helmert, 2006] to this domain and its factorization, using A with strong stubborn sets as well as IDA with sleep sets. Table 1 shows the resulting number of generated nodes (without the last f-layer to avoid tiebreaking issues) and the runtime in seconds.
Researcher Affiliation Academia University of Basel, Switzerland {martin.wehrle,silvan.sievers,malte.helmert}@unibas.ch
Pseudocode No The paper does not contain any clearly labeled pseudocode or algorithm blocks.
Open Source Code No The paper does not explicitly state that the source code for their methodology is publicly available, nor does it provide a link.
Open Datasets Yes As a proof of concept, we slightly modified the IPC Visitall domain such that moving and marking as visited are represented by different operators.
Dataset Splits No The paper mentions using the 'IPC Visitall domain' but does not specify any training, validation, or test dataset splits, percentages, or methodology for partitioning data.
Hardware Specification No The paper does not provide specific details about the hardware used to run the experiments (e.g., CPU, GPU models, or memory specifications).
Software Dependencies No The paper mentions using 'Fast Downward planner [Helmert, 2006]' but does not provide specific version numbers for this software or any other software dependencies.
Experiment Setup No The paper mentions using IDA with sleep sets and specific heuristics, but it does not provide specific experimental setup details such as hyperparameter values or system-level training settings.