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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Forgetting in Abstract Argumentation: Limits and Possibilities
Authors: Ringo Baumann, Matti Berthold, Dov Gabbay, Odinaldo Rodrigues
JAIR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide an in-depth analysis of desirable syntactical and/or semantical properties of possible forgetting operators. ... The analysis of desiderata, adapted to the specifics of abstract argumentation, includes implications among them, individual and collective satisfiability, and identifying inherent limits for a set of prominent semantics. Finally, we conduct a case study on stable semantics incorporating concrete forgetting operators. The paper extensively uses 'Propositions' and 'Proofs'. |
| Researcher Affiliation | Academia | Ringo Baumann EMAIL Universit at Leipzig, Germany Sca DS.AI, Dresden/Leipzig, Germany Matti Berthold EMAIL Sca DS.AI, Dresden/Leipzig, Germany Dov Gabbay EMAIL Odinaldo Rodrigues EMAIL King s College London, UK |
| Pseudocode | Yes | Construction 1. Given an AF F = (A,R) and X A. Let B = {a E E stb(FX) stb(F)}, s.t. B A = . Then, f1(F,X) = (A ,R ), where: A = A(FX) B, R = R(FX) {(b,b) b B} E stb(FX) stb(F) {(y,a E) y stb(FX) E}. The paper also includes 'Construction 2' and 'Construction 3' with similar structured algorithmic descriptions. |
| Open Source Code | No | The paper does not provide concrete access to source code. It states that 'Future work will include fine-tuning these constructions and conducting experiments on runtimes,' which implies the code is not currently released. |
| Open Datasets | No | The paper illustrates concepts with several small, self-contained examples (e.g., Example 1, Example 6) constructed for theoretical discussion. It does not refer to or provide access information for any publicly available or open datasets for empirical evaluation. |
| Dataset Splits | No | The paper does not mention dataset splits for training, testing, or validation. It focuses on theoretical analysis and uses illustrative examples rather than empirical evaluation on datasets. |
| Hardware Specification | No | The paper is theoretical and focuses on formal analysis and constructions. It does not describe any experimental setup involving specific hardware specifications like GPU or CPU models. |
| Software Dependencies | No | The paper is theoretical, describing logical frameworks and desiderata. It does not list specific software dependencies, libraries, or solvers with version numbers that would be used for experimental replication. |
| Experiment Setup | No | The paper presents theoretical analysis and formal constructions rather than empirical experiments. Therefore, it does not provide details on experimental setup, hyperparameters, or system-level training settings. |