A POMDP Approach to Influence Diagram Evaluation
Authors: Eric A. Hansen, Jinchuan Shi, Arindam Khaled
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | 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. |