Automatic Selection of Macro-Events for Heuristic-Search Temporal Planning
Authors: Alessandro La Farciola, Alessandro Valentini, Andrea Micheli
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we experimentally demonstrate that the proposed approach yields a substantial performance improvement for a state-of-the-art temporal planner. |
| Researcher Affiliation | Academia | Fondazione Bruno Kessler, Trento, Italy EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Prototypical Heuristic-Search Algorithm for Temporal Planning (with Macro-Events) |
| Open Source Code | Yes | Benchmarks, code and all the scatter plots are available at https://github.com/fbk-pso/step-rl. |
| Open Datasets | Yes | We considered the two benchmark domains in (Micheli and Valentini 2021) (enlarging the problem instances to make the problems more challenging). |
| Dataset Splits | Yes | For each domain, we perform a 4-fold splitting of the instances using the training part to construct the plan-macroevents and the testing part to evaluate our planner equipped with the selected set of macros against the baseline TAMER planner without macros. |
| Hardware Specification | Yes | We run our experiments on a server with 4 AMD EPYC 7413 processors and 528GB of RAM |
| Software Dependencies | No | The paper mentions 'Python' and the 'Unified Planning library' but does not provide specific version numbers for these software components. It also mentions TAMER without a version. |
| Experiment Setup | Yes | We chose as maximum length of candidates macro-events ℓmax = 5. The size of the extracted CME is 23619 for Kitting and 61378 for MAJSP. The average (wrt the 4-folds) time of execution for macros selection is 5s (Kitting) and 13s (MAJSP) in case FA-, 54s (Kitting) and 355s (MAJSP) in case PA-; in cases with intermediate nodes we allocated 30 minutes for the anytime algorithm. We run our experiments on a server with 4 AMD EPYC 7413 processors and 528GB of RAM, we allocated 4 cores for each planning run with a timeout of 600s and a memory limit of 40GB (the maximum memory used in the experiments was 6.4GB). |