Symbolic Search for Oversubscription Planning
Authors: David Speck, Michael Katz11972-11980
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An empirical analysis shows that our symbolic approach favorably competes with explicit state-space heuristic search, the current state of the art for oversubscription planning. Finally, an empirical study on various planning domains is conducted which shows that the presented algorithm results in an optimal planner that exceeds the current state of the art in terms of overall coverage. |
| Researcher Affiliation | Collaboration | David Speck1 and Michael Katz2 1 University of Freiburg, Freiburg im Breisgau, Germany 2 IBM T. J. Watson Research Center, Yorktown Heights, NY, USA |
| Pseudocode | Yes | Algorithm 1: SYM-OSP for unit cost OSP tasks |
| Open Source Code | Yes | 3Available online: https://github.com/speckdavid/symbolic-osp |
| Open Datasets | Yes | All experiments are conducted on the oversubscription planning benchmark set, based on the the optimal track from the International Planning Competition (IPC) 1998-2014 (Katz et al. 2019b), where goal facts are replaced with utilities. |
| Dataset Splits | No | The paper mentions using the 'oversubscription planning benchmark set' and discusses variations in cost bounds (25%, 50%, 75%, 100%), but it does not specify any training, validation, or test dataset splits or methodologies like k-fold cross-validation. |
| Hardware Specification | Yes | All experiments are run on a compute cluster with nodes equipped with two Intel Xeon Gold 6242 32-core CPUs, 20 MB cache and 188 GB shared RAM running Ubuntu 18.04 LTS 64 bit. |
| Software Dependencies | Yes | running Ubuntu 18.04 LTS 64 bit. and decision diagram library CUDD (Somenzi 2015) |
| Experiment Setup | Yes | The planners are build with 64 bit and run with a time limit of 30 minutes and memory limit of 4 GB. |