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..
On Structural Properties of MDPs that Bound Loss Due to Shallow Planning
Authors: Nan Jiang, Satinder Singh, Ambuj Tewari
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results with randomly generated MDPs are used to validate qualitative properties of our theoretical bounds for shallow planning. |
| Researcher Affiliation | Academia | 1Computer Science and Engineering, University of Michigan 2Department of Statistics, University of Michigan |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper uses 'randomly generated MDPs' according to specified schemes (fixed, binom, ring) rather than a publicly available or open dataset with a specific access link or citation. |
| Dataset Splits | No | The paper describes generating and sampling MDPs for empirical validation of theoretical bounds, but it does not specify traditional training, validation, or test dataset splits for a machine learning model or algorithm. |
| Hardware Specification | No | The paper discusses computational effort but does not provide specific details about the hardware (e.g., GPU, CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software components, libraries, or solvers used in the experiments. |
| Experiment Setup | Yes | Throughout the experiments we use ฮณeval = 0.995 and ฮณ = 0, 0.01, . . . , 0.99. |