Diagnosability of Discrete-Event Systems with Uncertain Observations
Authors: Xingyu Su, Marina Zanella, Alban Grastien
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The present paper provides an answer to this question when the observation is temporally or logically uncertain, that is, when the order of the observed events or their (discrete) values are partially unknown. The original notion of compound observable event enables a smooth extension of both the definition of DES diagnosability in the literature and the twin plant method to check such a property. |
| Researcher Affiliation | Collaboration | Artificial Intelligence Group, Australian National University, Australia; Optimisation Research Group, NICTA/Data61, Australia; Department of Information Engineering, University of Brescia, Italy |
| Pseudocode | No | The paper defines formal mathematical constructs (e.g., automata G//, G#d and their transition rules) but does not present any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any links to or statements about the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper uses a "running example" (Figure 1) which is a conceptual model used for illustration, not a dataset. There is no mention of a publicly available dataset being used for training or any other purpose. |
| Dataset Splits | No | The paper is theoretical and does not describe any experiments that would involve dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper focuses on theoretical concepts and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers that would be required to replicate an experimental setup. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details, such as hyperparameters or training configurations. |