Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
On Computing Explanations in Argumentation
Authors: Xiuyi Fan, Francesca Toni
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we propose a new argumentation semantics, related admissibility, designed for giving explanations to arguments in both Abstract Argumentation and Assumption-based Argumentation. We identify different types of explanations deο¬ned in terms of the new semantics. We also give a correct computational counterpart for explanations using dispute forests. |
| Researcher Affiliation | Academia | Xiuyi Fan and Francesca Toni EMAIL Department of Computing, Imperial College London, SW7 2AZ, UK |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for validation or training. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper describes theoretical concepts and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include details about an experimental setup, hyperparameters, or training configurations. |