What Makes You Special? Contrastive Heuristics Based on Qualified Dominance
Authors: Rasmus G. Tollund, Kim G. Larsen, Alvaro Torralba
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that our qualified dominance techniques are able to find information across many tasks, even though this is not very complementary with highly informative heuristics. We implemented our approach on top of Fast Downward [Helmert, 2006], as a constraint generation method for operator counting heuristics. We run experiments with Lab [Seipp et al., 2017] on AMD EPYC 7551 CPUs with memory/time cut-offs of 4 GBs and 30 minutes. Fig. 3 compares the expansions compared to dominance pruning with the same heuristic. We see a reasonable decrease of expansions in many tasks (e.g., of a factor of 2). Therefore, this can potentially be used for improving heuristic estimates. |
| Researcher Affiliation | Academia | Rasmus G. Tollund , Kim G. Larsen , Alvaro Torralba Aalborg University, Aalborg, Denmark EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Qualified Dominance Heuristic |
| Open Source Code | No | Code and experiment data will be made available upon publication. |
| Open Datasets | Yes | We use the Autoscale benchmark set [Torralba et al., 2021], consisting of 42 domains with 30 tasks in each. All tasks are automatically transformed into deterministic FTS tasks [Helmert, 2009; Sievers and Helmert, 2021]. |
| Dataset Splits | No | The paper mentions using the 'Autoscale benchmark set [Torralba et al., 2021]' consisting of '42 domains with 30 tasks in each', but does not specify how these tasks are split into training, validation, or test sets for their experiments, nor does it refer to predefined splits used for evaluation. |
| Hardware Specification | Yes | We run experiments with Lab [Seipp et al., 2017] on AMD EPYC 7551 CPUs with memory/time cut-offs of 4 GBs and 30 minutes. |
| Software Dependencies | No | The paper mentions implementing its approach on "Fast Downward [Helmert, 2006]" and running experiments with "Lab [Seipp et al., 2017]", but it does not specify explicit version numbers for these or any other ancillary software components used. |
| Experiment Setup | No | The paper describes methods such as "constraint generation method for operator counting heuristics" and combining them with "LM-cut [Helmert and Domshlak, 2009]" and "flow constraints". However, it does not provide specific hyperparameters (e.g., learning rates, batch sizes, epochs) or detailed training configurations for these methods. |