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..
Measuring Structural Similarities in Finite MDPs
Authors: Hao Wang, Shaokang Dong, Ling Shao
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally evaluate our proposed similarity measures using a series of designed MDPs. |
| Researcher Affiliation | Collaboration | 1Inception Institute of Artificial Intelligence, UAE 2State Key Laboratory for Novel Software Technology, Nanjing University, China |
| Pseudocode | Yes | Algorithm 1 shows the iterative algorithm for computing σ S and σ A by simulating the recursion of Sec. 4.1. |
| Open Source Code | No | The paper does not mention or provide any links to open-source code for the described methodology. |
| Open Datasets | No | The paper states, 'We generate a series of n n grid MDPs {Mn}...'. This indicates a self-generated dataset for which no public access information (link, DOI, citation) is provided. |
| Dataset Splits | No | The paper describes experiments on generated MDPs but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions algorithms like 'Dijkstra’s algorithm' and 'successive shortest path (SSP) algorithm' but does not specify any software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | In all the experiments, we set CS = CA = 0.95 for our solution and c R = 0.05 and c T = 0.95 for dbis. ... Algorithm 1 calculates two similarity matrices, Sn and An, with parameters CS = CA = 0.95... We first fix CS = 0.95 and vary CA from 0.80 to 0.99 with a step-size of 0.01. Then, we swap the roles of CS and CA. |