Semantics for Non-Flat Assumption-Based Argumentation, Revisited
Authors: Jesse Heyninck, Ofer Arieli
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we introduce a six-valued labelling semantics that overcomes these shortcomings while preserving all the usual properties of the standard Dung-style three-valued semantics for ABA frameworks, including existence of the complete semantics, uniqueness of the grounded semantics, and preservation of the computational complexity of all the main reasoning processes. and The claim concerning the grounded state (and the corresponding grounded labeling) follows from Proposition 2 and its proof, showing that the grounded extension can be computed by m N iterations of applying the G operator on given states, starting from s . By Lemma 6, each such application is computable in polynomial time. |
| Researcher Affiliation | Academia | Jesse Heyninck1,2 and Ofer Arieli3 1Department of Computer Science, Open Universiteit, Heerlen, the Netherlands 2University of Cape Town and CAIR, South-Africa 3School of of Computer Science, Tel-Aviv Academic College, Tel-Aviv, Israel |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | No | The paper is theoretical and uses examples for illustrative purposes; it does not utilize datasets for training or provide access information for any public dataset. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with dataset splits. Therefore, no specific dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe specific hardware used for running experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate experiments. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific details like hyperparameter values or training configurations. |