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 [1].
Unifying System Health Management and Automated Decision Making
Authors: Edward Balaban, Stephen B. Johnson, Mykel J. Kochenderfer
JAIR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We overview the prevalent system health management methodology, illustrate its limitations through numerical examples, and describe a proposed unified approach. We then show how typical system health management concepts are accommodated in the proposed approach without loss of functionality or generality. A computational complexity analysis of the unified approach is also provided. |
| Researcher Affiliation | Collaboration | Edward Balaban EMAIL NASA Ames Research Center Intelligent Systems Division Moffett Field, CA 94035 USA Stephen B. Johnson EMAIL Dependable System Technologies, LLC, Westminster, CO 80234, USA Mykel J. Kochenderfer EMAIL Department of Aeronautics and Astronautics Stanford University Stanford, CA 94305 USA |
| Pseudocode | No | The paper describes algorithms and frameworks but does not include any explicitly labeled pseudocode or algorithm blocks. It discusses concepts like value iteration, policy iteration, MCTS, POMCP, and DESPOT without presenting them in pseudocode format. |
| Open Source Code | No | The paper does not contain any statements about releasing source code for the methodology described, nor does it provide links to any code repositories. |
| Open Datasets | No | The paper uses a lunar rover example and specific numerical scenarios to illustrate its points, but it does not utilize or provide access to any publicly available or open datasets for empirical evaluation. |
| Dataset Splits | No | The paper does not use any datasets that would require splits. The examples provided are illustrative scenarios with predefined parameters, not empirical evaluations requiring data partitioning. |
| Hardware Specification | No | The paper describes theoretical concepts and numerical examples. It does not mention any specific hardware used for running experiments, such as GPU/CPU models, memory, or cloud platforms with specifications. |
| Software Dependencies | No | The paper discusses various algorithms and frameworks (e.g., MDPs, POMDPs, MCTS, DESPOT) but does not specify any particular software libraries, packages, or solvers with version numbers that were used in its own work. |
| Experiment Setup | No | The paper provides numerical examples with specific values for parameters like battery charge, power consumption, and probabilities. However, these are part of problem descriptions for conceptual illustration rather than detailed experimental setup parameters or hyperparameters for training a model. |