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..
Risk-Aware Scheduling throughout Planning and Execution
Authors: Andrew Wang
AAAI 2015 | Venue PDF | 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 EMAIL |
| 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. |