Generalized Label Reduction for Merge-and-Shrink Heuristics
Authors: Silvan Sievers, Martin Wehrle, Malte Helmert
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | As a case study, we implement a nonlinear merge strategy based on the original work on mergeand-shrink heuristics in model checking by Dr ager et al. We show experimental results that highlight the usefulness of generalized label reduction in general and non-linear merge strategies in particular. |
| Researcher Affiliation | Academia | Universit at Basel Basel, Switzerland {silvan.sievers,martin.wehrle,malte.helmert}@unibas.ch |
| Pseudocode | No | The paper describes methods in prose and mathematical notation, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using the Fast Downward planning system but does not provide any link or explicit statement about releasing the source code for the methodology described in this paper. |
| Open Datasets | Yes | We evaluate on all benchmarks from the International Planning Competitions for optimal planning (up to 2011) that only use language features supported by the merge-and-shrink framework (44 domains and 1396 instances in total). |
| Dataset Splits | No | The paper evaluates on a set of benchmark instances but does not describe a traditional train/validation/test split for a dataset, as it concerns heuristic generation and application for planning tasks rather than training a machine learning model. |
| Hardware Specification | Yes | The experiments were performed on Intel Xeon E5-2660 CPUs running at 2.2 GHz, using a time bound of 30 minutes and a memory bound of 2 GB per run. |
| Software Dependencies | No | The paper mentions using the 'Fast Downward planning system (Helmert 2006)' but does not provide specific version numbers for this system or any other software dependencies used in their implementation. |
| Experiment Setup | Yes | We varied along three dimensions: label reduction method, merge strategy and shrink strategy... We consider a shrink strategy based on greedy bisimulation with no limit on transition system size (G-N ) as well as shrink strategies based on (exact) bisimulation with different size limits N for the intermediate transition system size (B-N10k, B-N50k, B-N100k, BN200k, B-N ). The threshold parameter (Helmert et al. 2014) was set to N for the strategies with bounded transition system size and to 1 for the unbounded ones (G-N and B-N ), following Nissim, Hoffmann, and Helmert (2011a). |