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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Oversubscription Planning: Complexity and Compilability
Authors: Meysam Aghighi, Peter Jonsson
AAAI 2014 | Venue PDF | LLM Run Details | Input Tokens: 16,703 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,989 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The aim of this paper is two-fold: 1. to study the computational complexity of oversubscription planning under various restrictions, and 2. to present a new way of compiling oversubscription planning into classical planning. |
| Researcher Affiliation | Academia | Meysam Aghighi and Peter Jonsson Department of Computer and Information Science Link oping University Link oping, Sweden {meysam.aghighi, peter.jonsson} at liu.se |
| Pseudocode | No | The paper describes a 'Construction 1' in prose, but it is not formatted as pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper does not provide any information or links regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper focusing on complexity results and compilability; it does not describe experiments involving datasets. Therefore, no information about publicly available or open datasets is provided. |
| Dataset Splits | No | This is a theoretical paper focusing on complexity results and compilability; it does not describe experiments involving dataset splits for training, validation, or testing. |
| Hardware Specification | No | This is a theoretical paper. It does not describe any experimental setups or specify hardware used for computations. |
| Software Dependencies | No | This is a theoretical paper. While it discusses the 'SAS+ planning framework', it does not list any specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | This is a theoretical paper. It does not describe any experimental setups, hyperparameters, or system-level training settings. |