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..
Information-Theoretic Opacity-Enforcement in Markov Decision Processes
Authors: Chongyang Shi, Yuheng Bu, Jie Fu
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiment Evaluation Example 1 (Grid World Example). The effectiveness of the proposed optimal opacity-enforcement planning algorithms 1 is illustrated through a stochastic grid world example shown in Figure 1. |
| Researcher Affiliation | Academia | Chongyang Shi , Yuheng Bu and Jie Fu University of Florida EMAIL |
| Pseudocode | No | The paper describes algorithmic steps using mathematical equations and textual explanations, but it does not include any structured pseudocode blocks or algorithm listings labeled as 'Algorithm' or 'Pseudocode'. |
| Open Source Code | Yes | 1The code of the experiment is available on https://github.com/AronYoung414/leakage_minial_design_MDP |
| Open Datasets | No | The paper uses a custom 'Grid World Example' simulation environment and does not specify a publicly available dataset with concrete access information (link, DOI, or formal citation). |
| Dataset Splits | No | The paper describes a simulation environment and experiments but does not provide specific details on dataset splits (e.g., percentages, sample counts, or predefined split citations) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details (such as GPU/CPU models, memory, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., Python version, library names, or solver versions) needed to replicate the experiment. |
| Experiment Setup | Yes | We set the reward of reaching a goal to be 0.1 and the constraint that the total return is greater than or equals δ = 0.3, and the horizon T = 10. We will employ the soft-max policy parameterization, i.e., πθ(a|s) = exp(θs,a) / Σa' A exp(θs,a' ), where θ R|S A| is the policy parameter vector. As P1 enters these sensor ranges, the observer receives corresponding observations ( b , r , y , g , respectively) with probability p = 0.9 and a null observation ( 0 ) with probability 1 p = 0.1, attributed to the false negative rate of the sensors. |