Argumentation for Explainable Scheduling
Authors: Kristijonas Čyras, Dimitrios Letsios, Ruth Misener, Francesca Toni2752-2759
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We define three types of AFs, whose stable extensions are in one-to-one correspondence with schedules that are feasible, efficient and satisfying fixed decisions, respectively. We extract the argumentative explanations from these AFs and the natural language explanations from the argumentative ones. This work explicitly incorporates an example that assigns jobs to specific nurses. |
| Researcher Affiliation | Academia | Kristijonas ˇCyras, Dimitrios Letsios, Ruth Misener, Francesca Toni Imperial College London, London, UK |
| Pseudocode | No | The paper describes formal models and theorems but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information for source code, nor does it state that the code for its methodology is open-source. |
| Open Datasets | No | The paper is theoretical and uses abstract problem instances for illustrations (e.g., 'An instance I of the makespan scheduling problem'), but does not utilize or provide access to any publicly available or open datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets, therefore it does not provide specific dataset split information for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, therefore it does not provide specific hardware details used for running experiments. |
| Software Dependencies | No | The paper discusses abstract frameworks and theoretical concepts, but does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate an experiment. |
| Experiment Setup | No | The paper is theoretical and focuses on formal definitions and proofs; it does not describe an experimental setup with concrete hyperparameter values or training configurations. |