Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Abstraction Heuristics for Classical Planning Tasks with Conditional Effects
Authors: Martín Pozo, Jendrik Seipp
IJCAI 2025 | Venue PDF | 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. |