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..
Model-Based Diagnosis of Hybrid Systems Using Satisfiability Modulo Theory
Authors: Alexander Diedrich, Alexander Maier, Oliver Niggemann1452-1459
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For the experimental evaluation we use a simulation of the Tennessee Eastman process and a simulation of a four-tank model. We show that the presented approach is able to identify all injected faults. and Evaluation Empirical Evaluation Table 1 shows the experiments of the simulated four-tank model for constant input stream, the injected faults and whether or not the fault was detected. |
| Researcher Affiliation | Collaboration | Alexander Diedrich, Alexander Maier Fraunhofer IOSB-INA Fraunhofer Center for Machine Learning Lemgo, Germany EMAIL EMAIL Oliver Niggemann Institute Industrial IT Lemgo, Germany EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the public release of its source code. |
| Open Datasets | Yes | For the experimental evaluation we use a simulation of the Tennessee Eastman process and a simulation of a four-tank model. and The implementation of Downs et al. (Downs and Vogel 1993) was used which contains 20 different injected faults (process disturbances). |
| Dataset Splits | No | The paper discusses evaluating the algorithm at specific time-steps in the simulation ('time step 101'), but it does not provide details on traditional training, validation, or test dataset splits (percentages, counts, or predefined citations) for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific hardware details (such as CPU, GPU, or memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'the SMT solver z3' but does not specify its version number or any other software dependencies with version information. |
| Experiment Setup | No | The paper describes the simulation setup (e.g., 'constant input stream', '300 time-steps') and the theoretical framework, but it does not provide specific experimental setup details such as hyperparameters, optimization settings, or detailed model initialization values. |