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 [1].
Justifying Argument Acceptance with Collective Attacks: Discussions and Disputes
Authors: Giovanni Buraglio, Wolfgang Dvorak, Matthias König, Markus Ulbricht
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we studied discussion games with a special emphasis on the existence of collective attacks. We advocated for the use of SETAFs as an abstract argumentation formalism to represent conflicts arising from a given knowledge base. We introduced discussion games for SETAFs, generalizing their AF counterparts by characterizing credulous acceptance for preferred semantics (as a base case for many other semantics). Due to the increased expressive power of SETAFs, they yield more concise justifications in the presence of collective attacks. In particular, we investigated how the discussion game s length relates to the corresponding admissible set. Further, we introduced the notion of concise preferred discussions, a notion that is genuine for SETAFs and finds no counter-part in AFs. We show that this notion can be employed to reduce the size of discussions even further. To round up our investigation, we presented SETAF dispute trees in Section 5 and compared them to SETAF discussion games. Applying these notions to ABA demonstrated the improvements our proposal achieves. |
| Researcher Affiliation | Academia | Giovanni Buraglio1 , Wolfgang Dvoˇr ak1 , Matthias K onig1 and Markus Ulbricht2 1Institute of Logic and Computation, TU Wien 2Sca DS.AI Dresden/Leipzig, Leipzig University EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link about open-sourcing code for the described methodology. |
| Open Datasets | No | The paper uses illustrative examples (e.g., Example 1.1, Example 3.1) rather than empirical evaluation on datasets. Therefore, no information about dataset availability for training is provided. |
| Dataset Splits | No | The paper does not describe empirical experiments that would involve dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running experiments, as its contribution is theoretical. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |