Probabilistic Reasoning with Abstract Argumentation Frameworks
Authors: Anthony Hunter, Matthias Thimm
JAIR 2017 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Abstract argumentation offers an appealing way of representing and evaluating arguments and counterarguments. This approach can be enhanced by considering probability assignments on arguments, allowing for a quantitative treatment of formal argumentation. We consider constraints on these probability assignments, inspired by crisp notions from classical abstract argumentation frameworks and discuss the issue of probabilistic reasoning with abstract argumentation frameworks. Moreover, we consider the scenario when assessments on the probabilities of a subset of the arguments are given and the probabilities of the remaining arguments have to be derived, taking both the topology of the argumentation framework and principles of probabilistic reasoning into account. We generalise this scenario by also considering inconsistent assessments, i.e., assessments that contradict the topology of the argumentation framework. Building on approaches to inconsistency measurement, we present a general framework to measure the amount of conflict of these assessments and provide a method for inconsistency-tolerant reasoning. |
| Researcher Affiliation | Academia | Anthony Hunter EMAIL University College London, UK Matthias Thimm EMAIL University of Koblenz-Landau, Germany |
| Pseudocode | No | The paper describes methods and concepts through definitions, propositions, and theorems, such as Definition 3 for a probability function, Definition 4 for an epistemic labelling, and various propositions outlining relationships between different classes of probability functions. There are examples provided (e.g., Example 4, Example 9) that illustrate the theoretical concepts with specific values or scenarios, but these are textual descriptions and tables of values, not structured pseudocode or algorithm blocks. The paper does not contain any sections explicitly labeled "Pseudocode" or "Algorithm", nor does it present any procedure in a code-like format. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, nor does it provide links to code repositories. While Example 13 mentions using the "Open Opt optimization package" (http://openopt.blogspot.de) for determining inconsistency measure values, this refers to a third-party tool used for calculation illustrations, not the authors' own implementation of the paper's methodology. |
| Open Datasets | No | The paper is theoretical and focuses on developing a probabilistic framework for abstract argumentation. It uses abstract argumentation frameworks and illustrative examples (e.g., Figure 1, Figure 3, Figure 6, Figure 7) to demonstrate its concepts, definitions, and propositions. No specific, real-world datasets are mentioned or used for empirical evaluation. Therefore, there is no discussion of publicly available datasets, nor are any links, DOIs, or citations to datasets provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments using datasets. Therefore, there is no mention of training, testing, or validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and focuses on developing a probabilistic framework for abstract argumentation. It does not describe any empirical experiments that would require specific hardware. While Example 13 mentions the use of an "Open Opt optimization package" for calculations, it does not specify any hardware (e.g., GPU, CPU models, or cloud resources) on which these calculations were performed. The paper lacks any details regarding the hardware used for any computational work related to the described methodology. |
| Software Dependencies | No | The paper primarily presents a theoretical framework with definitions, propositions, and proofs. While Example 13 mentions that "Values of inconsistency measures were determined by using the Open Opt optimization package http://openopt.blogspot.de", this is a general reference to a third-party tool for calculation illustration, without specifying any version number for this package or any other software dependencies crucial for replicating the theoretical developments or specific examples. The paper does not list multiple key software components with their versions. |
| Experiment Setup | No | The paper is theoretical, presenting a probabilistic framework for abstract argumentation, and does not involve empirical experiments. Consequently, there are no experimental setup details, such as hyperparameter values, model initialization, training schedules, or any other configuration settings typically found in papers describing experimental results. |