Limits and Possibilities of Forgetting in Abstract Argumentation
Authors: Ringo Baumann, Matti Berthold
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that forgetting in abstract argumentation cannot be reduced to forgetting in logic programming. In addition, we deal with the more general problem of forgetting whole sets of arguments and show that iterative application of existing operators for single arguments does not necessarily yield a desirable result as it may not produce an informationally economic argumentation framework. As a consequence we provide a systematic and exhaustive study of forgetting desiderata and associated operations adapted to the intrinsics of abstract argumentation. We show the limits and shed light on the possibilities. ... Moreover, we consider forgetting under stable semantics as it shows a quite different behavior regarding the fulfillment of combined desiderata. Finally, we conclude and discuss related work. |
| Researcher Affiliation | Academia | Ringo Baumann and Matti Berthold Universit at Leipzig {baumann, berthold}@informatik.uni-leipzig.de |
| Pseudocode | Yes | Algorithm 1: Construct G = f 1 (F,X) ... Algorithm 2: Construct G = f 2 (F,X) |
| Open Source Code | No | No explicit statement about releasing source code or a link to a code repository was found. |
| Open Datasets | No | The paper is theoretical and uses illustrative examples rather than empirical datasets. No public dataset information for training was provided. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for validation. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments were mentioned. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., programming languages, libraries, or solvers) were mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe an empirical experiment setup with hyperparameters or training configurations. |