Time Decomposition for Diagnosis of Discrete Event Systems (Extended Abstract)
Authors: Xingyu Su
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, this work evaluates the performance of IWAs and TWAs measured by the precision of diagnosis, computational time, peak memory use, average memory use, and diagnostic distance. This work compares IWAs and TWAs in the above aspects with the exact diagnostic algorithm encoded by BDD [Schumann, 2007], named as Al0. This work also examines the impact of the time window size. The results of IWAs show that Al3 can achieve the same precision as using Al0 to diagnose a component-based DES model. Also, the run time and the average memory use are consistently reduced compared to using Al0. The results of Al3 indicate that using a larger time window, i.e., fewer time windows, leads to shorter computational time, as well as lower peak and average memory use than using a smaller time window. The results of TWAs demonstrate that Al5 and Al6 reduce the peak and average memory use compared to using Al0. However, the trade-off is that the computational time is longer than that of using Al0 due to the operations performed on abstract belief states between the time windows. |
| Researcher Affiliation | Academia | Xingyu Su The Australian National University, Australia u4383016@anu.edu.au |
| Pseudocode | No | The paper describes algorithms such as IWAs and TWAs, but does not include any specific pseudocode blocks, algorithm figures, or structured step-by-step procedures in the main text. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code, nor does it include links to a code repository for the methodology described. |
| Open Datasets | No | The paper discusses evaluation of algorithms on 'a component-based DES model' but does not specify any publicly available or open datasets by name, link, or formal citation. |
| Dataset Splits | No | The paper discusses evaluation metrics but does not provide specific details on training, validation, or testing dataset splits, percentages, or sample counts. |
| Hardware Specification | No | The paper discusses computational time and memory use, but does not provide any specific details about the hardware (e.g., CPU/GPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'BDD [Schumann, 2007]' in relation to Al0, but it does not specify any software libraries, tools, or dependencies with their version numbers required to reproduce the experiments. |
| Experiment Setup | Yes | This work also examines the impact of the time window size. The results of Al3 indicate that using a larger time window, i.e., fewer time windows, leads to shorter computational time, as well as lower peak and average memory use than using a smaller time window. |