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].
Robustness in Probabilistic Temporal Planning
Authors: Jeb Brooks, Emilia Reed, Alexander Gruver, James Boerkoel
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluation demonstrates that our robustness approximations better estimate plan success rate than previous metrics for determining the quality of a schedule. |
| Researcher Affiliation | Academia | Harvey Mudd College, Claremont, CA EMAIL |
| Pseudocode | Yes | Algorithm 1: Sampling-based Simulator; Algorithm 2: Representative Simulator |
| Open Source Code | No | Control code available upon request. |
| Open Datasets | No | The paper describes creating its own empirical distributions ('We empirically derived these pdfs by running our three robots around a one unit square 100 times each...'), but does not provide any access information (link, DOI, specific citation) for this dataset. |
| Dataset Splits | No | The paper describes running multi-robot scenarios 50 times but does not specify any training, validation, or test dataset splits in terms of percentages, sample counts, or references to predefined splits. |
| Hardware Specification | Yes | We tested our algorithms on an Intel Xeon E5-1603 2.80GHz quadcore processor with 8 GB of RAM running Ubuntu 12.04 LTS. |
| Software Dependencies | No | The paper mentions 'Pu LP, a Python library that leverages Coin MP an open source linear programming library' and 'Ubuntu 12.04 LTS', but it does not provide specific version numbers for software dependencies like Python, Pu LP, or Coin MP. |
| Experiment Setup | Yes | For the two approximation algorithms, we used the PF and PT pdfs displayed in Figure 5 as our models of durational uncertainty for the PSTN input to both the Sampling-based Simulator (N = 1000) and Representative Simulator. We empirically derived these pdfs by running our three robots around a one unit square 100 times each, and for each independent maneuver, building a histogram using a kernel smoother. |