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].
Value Compression of Pattern Databases
Authors: Nathan Sturtevant, Ariel Felner, Malte Helmert
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present extensive experimental results that illustrate the strengths and weaknesses of VC. We provide experimental results that support these trends. |
| Researcher Affiliation | Academia | Nathan R. Sturtevant Computer Science Department University of Denver EMAIL Ariel Felner ISE Department Ben-Gurion University Be er-Sheva, Israel EMAIL Malte Helmert Dept. of Math. and Computer Science University of Basel Switzerland EMAIL |
| Pseudocode | Yes | Algorithm 1: Optimal Partitioning |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described, nor does it mention a specific repository link or explicit code release statement. |
| Open Datasets | No | The paper describes using problem domains like (18,4)-Top Spin and 4-peg Towers of Hanoi and generates instances, e.g., '50 random instances which were created by 200 random steps.' However, it does not provide concrete access information (link, DOI, repository, or formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper mentions running experiments on '50 random instances' but does not provide specific details on train/validation/test dataset splits, percentages, or predefined split citations. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper describes problem parameters for the Top Spin domain (N, K, q) and how random instances were generated ('created by 200 random steps'). However, it does not provide specific experimental setup details such as hyperparameter values, detailed training configurations, or system-level settings typically found in machine learning experiments. |