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..
A POMDP Approach to Influence Diagram Evaluation
Authors: Eric A. Hansen, Jinchuan Shi, Arindam Khaled
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To illustrate the algorithm, and especially the performance of different heuristics for determining the order of reductions, we consider three IDs from the literature. ... Our algorithm solves this ID in a small fraction of a second regardless of which of these two orders of reductions is used. ... Surprisingly, the problem can be solved in less than a minute by our new node-removal/arc-reversal algorithm |
| Researcher Affiliation | Academia | Eric A. Hansen, Jinchuan Shi, and Arindam Khaled Dept. of Computer Science and Engineering Mississippi State University Mississippi State, MS 39762 |
| Pseudocode | No | The paper describes the algorithm verbally and mathematically but does not present a formal pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | We consider three IDs from the literature. ... [Raiffa, 1968] ... [Nilsson and Hohle, 2001] ... [Jensen and Nielsen, 2007, pp. 282-283] |
| Dataset Splits | No | The paper does not specify training, validation, or test dataset splits using percentages, sample counts, or references to predefined splits common in empirical machine learning. |
| Hardware Specification | No | The paper mentions performance times (e.g., "small fraction of a second", "less than a minute") but does not provide any specific details about the hardware (CPU, GPU models, memory, etc.) used for these computations. |
| Software Dependencies | No | The paper describes the proposed algorithm but does not list any specific software packages, libraries, or solvers with their version numbers that would be necessary to replicate the work. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size), optimizer settings, or other system-level training configurations typically found in empirical studies. |