Mixed Discrete-Continuous Heuristic Generative Planning Based on Flow Tubes
Authors: Enrique Fernandez-Gonzalez, Erez Karpas, Brian C. Williams
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | 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 Artiļ¬cial Intelligence Laboratory 32 Vassar Street, Building 32-224, Cambridge, MA 02139 efernan@mit.edu, karpase@mit.edu, williams@mit.edu |
| 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. |