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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Mixed Discrete-Continuous Heuristic Generative Planning Based on Flow Tubes
Authors: Enrique Fernandez-Gonzalez, Erez Karpas, Brian C. Williams
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Figure 6 we present a simple AUV sampling mission scenario that highlights this issue. ... While Kongming s performance degrades very fast with depth, Scotty s performance is constant (and orders of magnitude better than Kongming s). Table 3 shows Scotty s large performance advantage in other domains. |
| Researcher Affiliation | Academia | Enrique Fern andez-Gonz alez and Erez Karpas and Brian C. Williams Massachusetts Institute of Technology Computer Science and Arti๏ฌcial Intelligence Laboratory 32 Vassar Street, Building 32-224, Cambridge, MA 02139 EMAIL, EMAIL, EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks were found. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of its own methodology's code. |
| Open Datasets | No | The paper describes example scenarios (e.g., AUV mission) but does not use or provide access information for any publicly available or open datasets. |
| Dataset Splits | No | The paper evaluates its approach on problem scenarios but does not specify dataset splits (e.g., train/validation/test) in the traditional sense, as it does not rely on pre-existing datasets with such divisions for its experiments. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments were provided in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9) needed to replicate the experiment setup. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values, optimizer settings, or detailed training configurations. |