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..
Multi-Agent Abstract Argumentation Frameworks With Incomplete Knowledge of Attacks
Authors: Andreas Herzig, Antonio Yuste Ginel
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce a multi-agent, dynamic extension of abstract argumentation frameworks (AFs), strongly inspired by epistemic logic, where agents have only partial information about the con๏ฌicts between arguments. This version of multi-agent AFs, as well as their updates with public announcements of attacks (more concretely, the effects of these updates on the acceptability of an argument) can be described using S5-PAL, a well-known dynamic-epistemic logic. Results will be stated throughout this paper without proof due to space limitations, but they can be found in Antonio Yuste Ginel s forthcoming Ph D dissertation. |
| Researcher Affiliation | Academia | Andreas Herzig1 and Antonio Yuste Ginel2 1IRIT, CNRS 2University of M alaga EMAIL, EMAIL |
| Pseudocode | No | The paper describes a theoretical framework and its characterization using epistemic logic, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it state that code is released or available in supplementary materials. |
| Open Datasets | No | The paper is theoretical and does not involve empirical studies or datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical studies or datasets, therefore it does not provide dataset split information for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware for execution. |
| Software Dependencies | No | The paper describes a theoretical framework and its logical characterization, but it does not mention specific software dependencies or version numbers required to replicate any experiments or implementations. |
| Experiment Setup | No | The paper is theoretical and does not involve empirical experiments, therefore it does not provide specific experimental setup details like hyperparameters or training configurations. |