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..
Quantitative Claim-Centric Reasoning in Logic-Based Argumentation
Authors: Markus Hecher, Yasir Mahmood, Arne Meier, Johannes Schmidt
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we propose a concept for measuring the (acceptance) strength of claims, based on counting supports for a claim. Further, we settle classical and structural complexity of counting arguments favoring a given claim in propositional knowledge bases (KBs). As our first contribution, we categorize the classical and parameterized complexity of counting arguments to a claim (#ARG), as well as counting arguments where a given formula is also relevant (#ARG-Rel). We prove that both these problems are intractable and # co NPcomplete. |
| Researcher Affiliation | Academia | 1CSAIL, Massachusetts Institute of Technology, United States 2DICE group, Department of Computer Science, Paderborn University, Germany 3Institut f ur Theoretische Informatik, Leibniz Universit at Hannover, Germany 4Department of Computer Science and Informatics, J onk oping University, Sweden |
| Pseudocode | No | The paper does not contain any sections or blocks explicitly labeled as 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the methodology described. |
| Open Datasets | No | The paper focuses on theoretical analysis and does not describe empirical experiments involving datasets, thus no information on public dataset availability for training is provided. |
| Dataset Splits | No | The paper does not describe empirical experiments or data splits for training, validation, or testing. |
| Hardware Specification | No | The paper describes theoretical work and does not mention any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not list specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |