How Many Properties Do We Need for Gradual Argumentation?
Authors: Pietro Baroni, Antonio Rago, Francesca Toni
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we provide a systematic analysis for this research landscape by making three main contributions. First, we identify groups of conceptually related properties in the literature, which can be regarded as based on common patterns and, using these patterns, we evidence that many further properties can be considered. Then, we provide a simplifying and unifying perspective for these properties by showing that they are all implied by the parametric principles of (either strict or non-strict) balance and monotonicity. Finally, we show that (instances of) these principles are satisfied by several quantitative argumentation formalisms in the literature, thus confirming their general validity and their utility to support a compact, yet comprehensive, analysis of properties of gradual argumentation. |
| Researcher Affiliation | Academia | Pietro Baroni Dip.to di Ingegneria dell Informazione Universit a degli Studi di Brescia, Italy pietro.baroni@unibs.it Antonio Rago, Francesca Toni Dept. of Computing Imperial College London, UK {a.rago15,ft}@imperial.ac.uk |
| Pseudocode | No | The paper presents theoretical analysis and formal definitions but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention providing open-source code for the described methodology. |
| Open Datasets | No | The paper focuses on theoretical analysis of argumentation frameworks and does not use or reference specific datasets for training or empirical evaluation, thus no public dataset information is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments requiring dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware specifications used for experiments. |
| Software Dependencies | No | The paper focuses on theoretical analysis and does not list any specific software dependencies with version numbers for replication. |
| Experiment Setup | No | The paper is theoretical and does not include details on experimental setup, hyperparameters, or system-level training settings. |