Front-to-Front Heuristic Search for Satisficing Classical Planning
Authors: Ryo Kuroiwa, Alex Fukunaga
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally evaluated the following search algorithms, all of which were implemented by modifying the Fast Downward planning system. |
| Researcher Affiliation | Academia | Ryo Kuroiwa and Alex Fukunaga Graduate School of Arts and Sciences, The University of Tokyo mhgeoe@gmail.com, fukunaga@idea.c.u-tokyo.ac.jp |
| Pseudocode | Yes | Algorithm 1 Top-to-Top Bidirectional Search (TTBS) |
| Open Source Code | No | All code will be made available on a public repository. |
| Open Datasets | Yes | We used 1229 solvable instances in 47 planning domains from classical satisficing tracks in International Planning Competition (IPC) 98-2018, whose SAS+ representations do not contain axioms and conditional effects. |
| Dataset Splits | No | The paper evaluates performance on benchmark instances but does not describe a typical train/validation/test split for a dataset. |
| Hardware Specification | Yes | All runs were given a 5 min. time limit and a 4GB memory limit on Intel(R) Xeon(R) CPU E5-2670 v3 @ 2.30GHz processors. |
| Software Dependencies | No | The paper mentions software like 'Fast Downward planning system (FD)' and 'FDr', but does not provide specific version numbers for these or other ancillary software components, which are required for a reproducible description. |
| Experiment Setup | Yes | Differently from Alc azar et al. (2014), we used eager GBFS instead of lazy GBFS and did not use preferred operators. All methods used the unit-cost version of hff [Hoffmann and Nebel, 2001], which is straightforwardly applicable in regression and bidirectional search [Alc azar et al., 2013; Alc azar et al., 2014], using eager evaluation, and the FIFO tie-breaking strategy. All runs were given a 5 min. time limit and a 4GB memory limit. |