Robust Execution Strategies for Probabilistic Temporal Planning
Authors: Sam Dietrich, Kyle Lund, James Boerkoel
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluation To evaluate the efficacy of our approaches against each other and an early execution strategy, we generated random PSTNs with varying numbers of timepoints and constraint characteristics. ... As shown in Figure 3, DREA resulted in the most successful execution strategies. |
| Researcher Affiliation | Academia | Sam Dietrich and Kyle Lund and James C. Boerkoel Jr. Human Experience & Agent Teamwork Laboratory (http://cs.hmc.edu/HEAT/) Harvey Mudd College, Claremont, California 91711 {sdietrich, klund, boerkoel}@g.hmc.edu |
| Pseudocode | No | The paper describes algorithms but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention providing open-source code for the described methodology. |
| Open Datasets | No | The paper states 'we generated random PSTNs' but does not provide any access information (link, DOI, specific repository, or citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper mentions generating random PSTNs for evaluation but does not specify training, validation, or test dataset splits, percentages, or explicit methodology for partitioning data. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running experiments. |
| Software Dependencies | No | The paper mentions using an 'LP-formulation' but does not provide specific software names with version numbers for any libraries, solvers, or other ancillary software dependencies. |
| Experiment Setup | No | The paper describes aspects of its algorithms (e.g., finding risk-level α, expanding bounds by δ) but does not provide specific experimental setup details such as hyperparameters, model initialization, or detailed training configurations. |