Risk-Aware Scheduling throughout Planning and Execution
Authors: Andrew Wang
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The runtime performance of our conflict-directed approach is shown in Figure 2 to rival prior art by about an order of magnitude. |
| Researcher Affiliation | Academia | Andrew J. Wang Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology wangaj@mit.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link confirming that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper does not provide concrete access information (link, DOI, repository, or formal citation) for a publicly available dataset used in its experiments. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions leveraging existing software components and algorithms but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |