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 [1].
Decoupled Strong Stubborn Sets
Authors: Daniel Gnad, Martin Wehrle, Jörg Hoffmann
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Table 1 shows coverage results. |
| Researcher Affiliation | Academia | Daniel Gnad Saarland University Saarbr ucken, Germany EMAIL Martin Wehrle University of Basel Basel, Switzerland EMAIL J org Hoffmann Saarland University Saarbr ucken, Germany EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found. |
| Open Source Code | No | The paper mentions extending an existing implementation ("We extended GH s implementation of fork-decoupled search in FD [Helmert, 2006]") but does not provide a link or explicit statement about releasing the source code for the current work. |
| Open Datasets | Yes | From the International Planning Competition (IPC) STRIPS benchmarks ( 98 14), this is the case for instances from 12 domains. |
| Dataset Splits | No | The paper evaluates on instances from the International Planning Competition (IPC) STRIPS benchmarks. It does not mention specific training, validation, or test dataset splits in the context of machine learning model training. |
| Hardware Specification | Yes | All experiments are run on a cluster of Intel E5-2660 machines running at 2.20 GHz, with time (memory) cut-offs of 30 minutes (4 GB). |
| Software Dependencies | No | The paper mentions extending 'GH s implementation of fork-decoupled search in FD [Helmert, 2006]' but does not provide specific version numbers for FD or any other software libraries or dependencies. |
| Experiment Setup | Yes | We run A with a blind heuristic as a measure of search space size, and with LM-cut [Helmert and Domshlak, 2009] as a representative of the state of the art, using GH s method (Fork-Decoupled A ) to adopt these techniques for decoupled search. We compare decoupled search with DSSS pruning (simply referred to as DSSS in what follows) against decoupled search without that pruning ( DS in what follows). We furthermore compare against A in the standard state space without pruning ( A in what follows), and with SSS pruning ( SSS in what follows). |