Higher-Order Argumentation Frameworks: Principles and Gradual Semantics
Authors: Leila Amgoud, Dragan Doder, Marie-Christine Lagasquie-Schiex
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper investigates how to evaluate elements in complex argumentation frameworks, where both arguments and attacks are weighted and might be attacked by arguments. We propose the first gradual semantics that assign a numerical value to every argument and attack. The value represents the acceptance (seriousness) degree of an argument (attack). We start by highlighting various technical challenges facing semantics in such complex settings, including how to deal with attacks vs arguments, and how to combine their values. We present principles that describe different strategies offered to semantics to address such challenges. Then, we introduce various semantics per strategy. For instance, some semantics evaluate attacks and arguments in the same way while others, called hybrid, treat them differently. Finally, the principles are used to compare the plethora of novel semantics. The final result is a catalogue of semantics with different formal guarantees and behaviours. (From Abstract). A first trivial implementation of the new gradual semantics has already been carried out. The results show that they are efficient even in case of large and complex HO-AFs. (From Conclusion) - The paper primarily focuses on the definition of principles, formal semantics, and a formal analysis of their properties, rather than empirical evaluation with data and metrics. The mention of a 'trivial implementation' is high-level and lacks the specifics to qualify as experimental research. |
| Researcher Affiliation | Academia | Leila Amgoud1 , Dragan Doder2 and Marie-Christine Lagasquie-Schiex3 1CNRS IRIT, France 2Utrecht University, Netherlands 3Universit e Toulouse 3 IRIT, France |
| Pseudocode | No | The paper does not contain any structured pseudocode blocks or algorithms. |
| Open Source Code | No | The paper does not provide any specific link or statement regarding the release of source code for the methodology described. |
| Open Datasets | No | The paper uses 'Example 1' for illustration, which is a small, hand-crafted example, not a publicly available dataset with concrete access information (link, DOI, or formal citation). |
| Dataset Splits | No | The paper does not use a dataset for empirical evaluation, hence it does not specify any training/validation/test dataset splits. |
| Hardware Specification | No | The paper states 'A first trivial implementation of the new gradual semantics has already been carried out' but does not specify any hardware details (e.g., CPU, GPU, memory, or cloud resources) used for this implementation or any experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers needed to replicate any implementation or experiments. |
| Experiment Setup | No | The paper does not detail any experimental setup, hyperparameters, or system-level training settings for reproducing results, as its primary focus is theoretical. |