Quantitative Claim-Centric Reasoning in Logic-Based Argumentation
Authors: Markus Hecher, Yasir Mahmood, Arne Meier, Johannes Schmidt
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | 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. |