Quantitative Reasoning and Structural Complexity for Claim-Centric Argumentation

Authors: Johannes K. Fichte, Markus Hecher, Yasir Mahmood, Arne Meier

IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We introduce a novel concept of a justification status for claims, a quantitative measure of extensions supporting a particular claim. Furthermore, we explore the parameterized complexity of various reasoning problems for CAFs, including the quantitative reasoning for claim assertions. We establish tight runtime bounds for treewidth that cannot be improved under reasonable complexity assumptions.
Researcher Affiliation Academia 1Link oping University, Sweden 2Massachusetts Institute of Technology, USA 3DICE group, Paderborn University, Germany 4Institut f ur Theoretische Informatik, Leibniz Universit at Hannover, Germany
Pseudocode No The paper presents algorithms and reductions using mathematical notation and descriptive text, but no explicit pseudocode blocks or algorithms labeled as such.
Open Source Code No The paper does not state that source code for its methodology is made available, nor does it provide any links to a code repository.
Open Datasets No The paper is a theoretical work focusing on complexity analysis and does not involve empirical evaluation using datasets. Therefore, no information about dataset availability is provided.
Dataset Splits No The paper is a theoretical work and does not involve empirical validation or dataset splits for training, validation, or testing.
Hardware Specification No The paper is theoretical and does not describe any experiments that would require specific hardware for execution. Therefore, no hardware specifications are mentioned.
Software Dependencies No The paper discusses theoretical concepts related to QBF-solvers but does not specify any software dependencies with version numbers used for its own work or analysis.
Experiment Setup No The paper is theoretical and does not include details about an experimental setup, hyperparameters, or training configurations.