A Generalization of Sleep Sets Based on Operator Sequence Redundancy
Authors: Robert C. Holte, Yusra Alkhazraji, Martin Wehrle
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On a practical level, our experimental evaluation shows the potential of sleep sets and their generalizations on a large and common set of planning benchmarks. We implemented sleep sets and move pruning within the Fast Downward planning system (Helmert 2006). We evaluate these pruning techniques with IDA , with cycle detection, using the LM-Cut heuristic (Helmert and Domshlak 2009), which is the state-of-the-art admissible planning heuristic, on all the planning benchmarks from the international planning competitions up to 2011 that contain the features required by LM-Cut (1396 problem instances drawn from 44 domains). |
| Researcher Affiliation | Academia | Robert C. Holte University of Alberta, Canada robert.holte@ualberta.ca Yusra Alkhazraji University of Freiburg, Germany alkhazry@informatik.uni-freiburg.de Martin Wehrle University of Basel, Switzerland martin.wehrle@unibas.ch |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions implementing within the Fast Downward planning system, but does not provide a specific repository link or an explicit statement of releasing the code for the methodology described in this paper. |
| Open Datasets | Yes | We evaluate these pruning techniques... on all the planning benchmarks from the international planning competitions up to 2011 that contain the features required by LM-Cut (1396 problem instances drawn from 44 domains). |
| Dataset Splits | No | The paper refers to using '1396 problem instances drawn from 44 domains' from international planning competitions but does not provide specific dataset split information (percentages, sample counts, or methodology) needed to reproduce the data partitioning into train, validation, or test sets. |
| Hardware Specification | Yes | The evaluation is performed on Intel Xeon E5-2660 CPUs that run at 2.2 GHz, with a time limit of 30 minutes (for the total of search time and preprocessing time) and a memory limit of 2 GB per run. |
| Software Dependencies | No | The paper mentions software like 'Fast Downward planning system' and 'LM-Cut heuristic' but does not provide specific version numbers for these or other ancillary software components needed to replicate the experiment. |
| Experiment Setup | Yes | We evaluate these pruning techniques with IDA , with cycle detection, using the LM-Cut heuristic (Helmert and Domshlak 2009)... For move pruning we set L = 2. Times that are less than or equal to 0.1 seconds are shown in the plots as 0.1. ... with a time limit of 30 minutes (for the total of search time and preprocessing time) and a memory limit of 2 GB per run. |