Finding Minimal Plan Reductions Using Classical Planning
Authors: Mauricio Salerno, Raquel Fuentetaja, Jendrik Seipp
JAIR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we evaluate the new approaches experimentally and show that they are competitive with the previous state of the art in minimal plan reduction. We carry out a comprehensive evaluation, aimed at empirically comparing the different classical planning-based approaches among themselves and with the weighted Max SAT approach; and obtaining empirical evidence for the identified situations where a planning approach is preferable to weighted Max SAT. Section 7 is titled "Evaluation" and presents benchmark analysis, comparison of planning compilations, and results with different heuristics, including tables and figures of empirical data. |
| Researcher Affiliation | Academia | MAURICIO SALERNO, Universidad Carlos III de Madrid, España RAQUEL FUENTETAJA, Universidad Carlos III de Madrid, España JENDRIK SEIPP, Linköping University, Sweden. Authors Contact Information: Mauricio Salerno, orcid: 0000-0002-7664-5847, EMAIL, Universidad Carlos III de Madrid, Leganés, Madrid, España; Raquel Fuentetaja, orcid: 0000-0002-3856-2629, EMAIL, Universidad Carlos III de Madrid, Leganés, Madrid, España; Jendrik Seipp, orcid: 0000-0002-2498-8020, EMAIL, Linköping University, Linköping, Sweden. |
| Pseudocode | Yes | Algorithm 1 Compute fix-point plan action landmarks. 1: function Compute FPALs(Π, 𝜋) 2: 𝑎0,𝑎1, . . . ,𝑎𝑛,𝑎𝑛+1 𝜋 3: PALs {𝑎𝑛+1} 4: achs Compute Achievers(𝜋) Initial achievers 5: repeat 6: for 𝑖= 𝑛+ 1 to 0 do 7: if 𝑎𝑖 PALs then 8: for 𝑣 𝑑 pre(𝑎𝑖) do 9: 𝐴 {𝑎𝑗| 𝑎𝑗,𝑘 achs(𝑣 𝑑), 𝑗< 𝑖 𝑘} 10: if 𝐴= {𝑎𝑗} and 𝑎𝑗 PALs then Check if fact has a single achiever 11: PALs PALs {𝑎𝑗} 12: Update Achievers(achs,𝑎𝑗) 13: until PALs remains unchanged 14: return PALs 16: procedure Update Achievers(achs,𝑎𝑗) Update achievers 17: for 𝑣 𝑑 eff (𝑎𝑗) do 18: for 𝑑 D(𝑣) ", with further steps. |
| Open Source Code | Yes | Code and data for the planning compilations are available online (Salerno et al. 2025). M. Salerno, R. Fuentetaja, and J. Seipp. 2025. Code and Data for the Article Finding Minimal Plan Reductions Using Classical Planning . https://doi.org/10.5281/zenodo.14065819. (2025). |
| Open Datasets | Yes | As benchmarks, we use the same tasks and input plans as Med and Chrpa 2022. The benchmark set consists of tasks from the Agile tracks of the International Planning Competitions (IPC) of 2014 and 2018, and plans found with the following planners: Cerberus (Katz 2018), Freelunch Madagascar (Balyo and Gocht 2018), LAMA 2011 (Richter, Westphal, and Helmert 2011), BFWS Preference (Francès et al. 2018) and YAHSP3 (Vidal 2014). |
| Dataset Splits | No | The paper uses benchmark sets of planning tasks and input plans from International Planning Competitions (IPC). It does not specify conventional training, validation, or test dataset splits in terms of percentages or sample counts, as its context is classical planning tasks rather than machine learning datasets requiring such splits. |
| Hardware Specification | No | All algorithm runs are limited to a runtime of 30 minutes and 8 Gi B of memory. No specific CPU or GPU models, processor types, or detailed computer specifications are mentioned for the experimental setup. |
| Software Dependencies | No | We use Python 3.10 to generate the planning task reformulations. To generate the WPMax SAT tasks formulations we use the Java code by Balyo, Chrpa, et al. 2014. To solve the WPMax SAT formulations, we use the Sat4J solver (Le Berre and Parrain 2010). The paper mentions Python 3.10 with a specific version, but other key software components like Scorpion planning system, Fast Downward, Java code, and Sat4J solver are mentioned without specific version numbers. |
| Experiment Setup | Yes | All algorithm runs are limited to a runtime of 30 minutes and 8 Gi B of memory. We use 𝐴 with an admissible heuristic (described below) to find optimal plans. We use Πs1 a1 enhanced with FPALs for all experiments below. The paper also discusses and compares various heuristics: ℎmax, ℎ0, ℎLM-cut, ℎBJOLP, and ℎSCP. |