tBurton: A Divide and Conquer Temporal Planner
Authors: David Wang, Brian Williams
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We describe why this approach is fast and efficient, and demonstrate its ability to improve the performance of existing planners on factorable problems through benchmarks from the International Planning Competition. We benchmarked t Burton on a combination of IPC 2011 and IPC 2014 domains. Temporal Fast Downward (TFD) from IPC 2014 was used as an off-the-shelf subplanner for t Burton because it was straight-forward to translate TCAs to the SAS representation used by TFD (Helmert 2006). For comparison, we also benchmarked against YAHSP3-MT from IPC 2014, POPF2 from IPC 2011, and TFD from IPC 2014, the winner or runners up in the 2011 and 2014 temporal satisficing track (Table 1). |
| Researcher Affiliation | Academia | David Wang and Brian Williams Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, Massachusetts 02139 |
| Pseudocode | Yes | Algorithm 1 provides the pseudo code for t Burton s high-level search algorithm. |
| Open Source Code | No | The paper does not provide an unambiguous statement or link for the open-source code of the methodology described in the paper. |
| Open Datasets | Yes | We benchmarked t Burton on a combination of IPC 2011 and IPC 2014 domains. |
| Dataset Splits | No | The paper benchmarks the planner on IPC domains and reports problems solved and IPCScore, but it does not specify explicit training, validation, and test dataset splits or cross-validation details for the experimental setup. The IPC domains are problem sets for testing planners, not typically split into train/validation/test sets for model training in the conventional sense. |
| Hardware Specification | No | The tests were run with scripts from IPC 2011 and were limited to 6GB memory and 30 minute runtime. No specific hardware (GPU/CPU models, etc.) is mentioned. |
| Software Dependencies | No | Temporal Fast Downward (TFD) from IPC 2014 was used as an off-the-shelf subplanner for t Burton... For comparison, we also benchmarked against YAHSP3-MT from IPC 2014, POPF2 from IPC 2011, and TFD from IPC 2014... While it names other planners with their IPC year, it does not specify exact version numbers for other ancillary software or libraries. |
| Experiment Setup | No | The paper states: "The tests were run with scripts from IPC 2011 and were limited to 6GB memory and 30 minute runtime." This indicates some constraints, but it does not provide specific hyperparameters, optimizer settings, or detailed training configurations as typically found in experimental setup sections for machine learning models. The paper is about a planner, not a learnable model, so such details might not apply in the same way. |