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].
Stochastic Planning in Large Search Spaces
Authors: Bilal Kartal
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | finds near-optimal solutions for non-trivial problems in an existing test benchmarks within an hour." and "A near-optimal task allocation policy found by our parallelized approach with an approximation rate of 1.03 to an optimal solution is shown. |
| Researcher Affiliation | Academia | Bilal Kartal Department of Computer Science and Engineering University of Minnesota EMAIL |
| Pseudocode | No | The paper describes the methods in prose but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides links to videos of the system in action (e.g., http://motion.cs.umn.edu/r/MCTS-UC and http://motion.cs.umn.edu/r/Story MCTS) but does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper refers to 'existing test benchmarks' but does not provide concrete access information (e.g., specific names, links, or citations with authors/year) for any publicly available or open datasets used. |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, or methodology) needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the approach and its components (e.g., 'parameterized root parallelization', 'varying exploration parameters') but does not provide specific experimental setup details such as concrete hyperparameter values or training configurations. |