Abstraction Heuristics for Classical Planning Tasks with Conditional Effects
Authors: Martín Pozo, Jendrik Seipp
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that these heuristics are competitive with and often surpass the state-of-the-art for conditional-effect tasks. |
| Researcher Affiliation | Academia | 1Universidad Carlos III de Madrid 2Link oping University EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: CEGAR loop for a task Π. Algorithm 2: Compute outgoing transitions from abstract state a via operator o in a projection to pattern P. Algorithm 3: Compute outgoing transitions from abstract state a via operator o in Cartesian abstraction α. |
| Open Source Code | Yes | All code, benchmarks, and experiment data is available online [Pozo and Seipp, 2025]. |
| Open Datasets | Yes | As our benchmark set, we use the domains with conditional effects from the last two IPCs, the domains used by R oger et al. [2014], the matrix multiplication domain [Speck et al., 2023], and the domain for transforming quantum circuits into CNOT-only layouts [Shaik and van de Pol, 2024]. |
| Dataset Splits | No | The paper uses established benchmark domains for planning tasks, which are typically defined by a set of problems rather than explicit training/validation/test splits in the machine learning sense. No specific dataset split information (percentages, sample counts, or explicit split files) is provided in the text for reproducibility. |
| Hardware Specification | Yes | We use Downward Lab [Seipp et al., 2017] and run experiments on Xeon Gold 6130 processors. |
| Software Dependencies | No | The paper mentions using the "Scorpion planning system [Seipp et al., 2020a], which is an extension of Fast Downward [Helmert, 2006]" and the "h2 preprocessor [Alc azar and Torralba, 2015]", but specific version numbers for these software components are not provided in the text. |
| Experiment Setup | Yes | For all configurations we use the h2 preprocessor [Alc azar and Torralba, 2015] and limit time and memory for each planner run to 30 minutes and 8 Gi B, respectively. ... For M&S, we use the recommended values for all parameters [Sievers, 2018] and up to 100 000 abstract states. For Cartesian abstractions, we use incremental search to find abstract plans [Seipp et al., 2020b] and a time limit of 900 seconds for the CEGAR loop. |