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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Strong Explanations in Abstract Argumentation
Authors: Markus Ulbricht, Johannes P. Wallner6496-6504
AAAI 2021 | Venue PDF | 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 EMAIL, EMAIL |
| 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. |