Strong Explanations in Abstract Argumentation
Authors: Markus Ulbricht, Johannes P. Wallner6496-6504
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We formally show that strong explanations form a larger class than extensions, in particular giving the possibility of having smaller explanations. Moreover, assuming basic properties, we show that any explanation strategy, broadly construed, is a strong explanation. We show that the increase in variety of strong explanations comes with a computational trade-off: we provide an in-depth analysis of the associated complexity, showing a jump in the polynomial hierarchy compared to extensions. |
| Researcher Affiliation | Academia | Markus Ulbricht,1 Johannes P. Wallner2 1 Department of Computer Science, Leipzig University, Germany 2 Institute of Software Technology, Graz University of Technology, Austria mulbricht@informatik.uni-leipzig.de, wallner@ist.tugraz.at |
| Pseudocode | Yes | For given sets E, X A and AF F = (A, R), for each a E perform the following: 1. E := E \ {a} 2. E := E \ {a E | a not defended by E in F} 3. if E = or E ad(F) terminate, otherwise go to 2. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not mention any datasets or training data. |
| Dataset Splits | No | The paper is theoretical and does not mention any validation splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide any specific experimental setup details such as hyperparameters or training configurations. |