Modelling the Persuadee in Asymmetric Argumentation Dialogues for Persuasion
Authors: Anthony Hunter
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we consider asymmetric dialogues where only the system presents arguments, and the system maintains a model of the user to determine the best choice of arguments to present (including counterarguments to key arguments believed to be held by the user). The focus of the paper is on the user model, including how we update it as the dialogue progresses, and how we use it to make optimal choices for dialogue moves. We base our paper on abstract argumentation [Dung, 1995]. |
| Researcher Affiliation | Academia | Anthony Hunter Department of Computer Science, University College London, London, UK anthony.hunter@ucl.ac.uk |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It describes methods and functions via definitions and propositional logic. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described, such as a repository link or an explicit code release statement. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any publicly available or open datasets for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments, therefore it does not provide specific dataset split information. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments, thus it does not specify any hardware details. |
| Software Dependencies | No | The paper is theoretical and does not describe experiments or software implementations, thus it does not list specific ancillary software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe experiments, thus it does not provide specific experimental setup details such as hyperparameter values or training configurations. |