Resistance to Corruption of Strategic Argumentation
Authors: Michael Maher
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We investigate it in the context of Dung s abstract argumentation. We show that strategic argumentation under the grounded semantics is resistant to corruption specifically, collusion and espionage in a sense similar to Bartholdi et al s notion of a voting scheme resistant to manipulation. Under the stable semantics, strategic argumentation is resistant to espionage, but its resistance to collusion varies according to the aims of the disputants. These results are extended to a variety of concrete languages for argumentation. |
| Researcher Affiliation | Academia | Michael J. Maher School of Engineering and Information Technology UNSW, Canberra E-mail: michael.maher@unsw.edu.au |
| Pseudocode | No | The paper is theoretical and focuses on complexity analysis. It does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper presents theoretical results on computational complexity of strategic argumentation. It does not mention or provide access to any open-source code for a described methodology. |
| Open Datasets | No | The paper is theoretical and analyzes computational complexity. It does not involve the use of datasets for training, validation, or testing. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets, therefore no dataset split information for validation is provided. |
| Hardware Specification | No | The paper is theoretical and discusses computational complexity. It does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and focuses on complexity analysis. It does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on complexity analysis. It does not describe an empirical experimental setup with hyperparameters or training settings. |