K∗ Search over Orbit Space for Top-k Planning
Authors: Michael Katz, Junkyu Lee
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We prove that our algorithm is sound and complete for top-k planning and empirically show it to achieve state-of-the-art performance, overtaking all existing to date top-k planners. |
| Researcher Affiliation | Industry | Michael Katz , Junkyu Lee IBM T.J. Watson Research Center, Yorktown Heights, USA {michael.katz1, junkyu.lee}@ibm.com |
| Pseudocode | Yes | Algorithm 1 OK Search |
| Open Source Code | Yes | The code is available at https://github.com/IBM/kstar. |
| Open Datasets | Yes | The benchmark set consists of all benchmarks from optimal tracks of International Planning Competitions 1998-2018, a total of 1827 tasks in 65 domains. |
| Dataset Splits | No | The paper mentions using benchmarks from International Planning Competitions, but it does not specify any dataset split information (percentages, sample counts, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | Yes | All experiments were performed on Intel(R) Xeon(R) Gold 6248 CPU @ 2.50GHz machines, with the timeout of 30 minutes and memory limit of 8GB per run. |
| Software Dependencies | No | The paper mentions using the 'Fast Downward planning system' and various heuristics (LMcut, M&S, CEGAR, i PDB), but it does not provide specific version numbers for any of these software dependencies. |
| Experiment Setup | Yes | All experiments were performed on Intel(R) Xeon(R) Gold 6248 CPU @ 2.50GHz machines, with the timeout of 30 minutes and memory limit of 8GB per run. |