Institute for Web Science and Technologies · Universität Koblenz
Institute WeST


Learning Argumentation Frameworks from Labelings

30.09.21. Since its introduction by Dung in 1995, a central aspect of abstract argumentation has been the argumentation semantics. These semantics are concerned with computing sets of arguments which satisfy certain criteria based on the structure of attacks in the argumentation framework. Two approaches can be distinguished for the output of argumentation semantics: extensions and labelings. In the scope of this work we will mainly focus on labeling-based argumentation semantics. A topic which has not received as much attention so far is going in the opposite direction of semantics: Constructing argumentation frameworks from a given set of labelings such that these argumentation frameworks produce all input labelings. This master thesis presents a novel approach for learning argumentation frameworks from labelings. An important element of this approach is computing all argumentation frameworks that satisfy the input labelings instead of simply finding any suitable argumentation framework. This is especially important, for example, if we receive additional labelings at a later time and want to refine our result without having to start all over again. The developed algorithm will be compared to the existing work on this topic and an evaluation of its performance has been conducted. [read more...]

Complexity of Nonemptiness in Control Argumentation Frameworks

16.09.21. Control argumentation frameworks (CAFs) extend the standard model of argumentation framework (AF) due to Dung, one of the most central frameworks to model discussions among agents in argumentation theory, in a way that takes unquantified uncertainty regarding arguments and attacks into account. Complementing recent work by Skiba et al. for incomplete argumentation frameworks, we study the (nonempty) existence problem for CAFs and fully analyze its computational complexity for the most common semantics. [read more...]

Using neural networks for approximate approaches as a heuristic to exact methods with abstract argumentation frameworks.

22.07.21. argumentation is a method for providing abstractions of problems along three spectrums: arguments, attacks, and acceptability, the latter the most important property of a semantic. Exact approaches, often using reduction to some other formalism such as SAT and ASP, are known to be computationally hard and hence, difficult to solve for realistic models. In addressing those issues, this research firstly implements neural networks to predict credulous acceptability of abstract arguments, as a classification problem. Secondly, we propose to implement an efficient heuristic by using the approximate method to set warm-start point, minimize backtracks steps and maximize the performance of a complete solution, so-called DREED, to abstract argumentation problems. To the best of our knowledge, this combination has not been explored yet within the argumentation community. [read more...]

Deep Learning for Differential Diagnosis and Prediction in EHR Data

08.07.21. Over the last decade, the generation of massive Electronic Health Records (EHR) allowed researchers to explore the secondary use of these data in the field of biomedical informatics researches. Recent researches showed that deep learning models are efficient in collecting important features from EHR data and predict a disease diagnosis. However, these models performed inadequately when it comes to extracting important features from heterogeneous EHR data and predicting multiple disease outcomes. This thesis aims to provide a method to generalize different EHR structures and then train a deep learning model to predict multiple disease outcomes. Thereby, the model would help in differential diagnosis where multiple other disease outcomes are identified given some symptoms. To the best of my knowledge, this is the first time a deep learning model would be used in differential disease diagnosis prediction. [read more...]

Revisiting minimal admissible sets in abstract argumentation

01.07.21. We introduce elementary cores as sets of arguments of an abstract argumentation framework that are minimally admissible for each of its members. Elementary cores are used to decompose arbitrary admissible sets and characterise certain admissibility-based semantics. Elementary cores can then be used to explain the reasoning process behind these semantics using a simple rule transition system. [read more...]

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