Oversubscription Planning: Complexity and Compilability
Authors: Meysam Aghighi, Peter Jonsson
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |