Matthias Thimm

Matthias Thimm Senior researcher at the Institute for Web Science and Technologies (WeST) in Koblenz, Germany

Title: Reasoning under Uncertainty with Abstract Argumentation Frameworks

Abstract: Abstract argumentation offers an appealing way of representing and evaluating arguments and counterarguments. This approach can be enhanced by considering probability assignment on arguments, allowing for a quantitative treatment of formal argumentation. In this talk, we regard the assignment as denoting the belief that an agent has that an argument is justifiable, i. e., that both the premises of the argument and the derivation of the claim of the argument from its premises are valid. We consider various 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 generalize 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 inconsistent-tolerant decision-making.

Biography: Matthias Thimm is senior researcher at the Institute for Web Science and Technologies (WeST) in Koblenz, Germany. I received my PhD degree from the University of Dortmund (Germany) in 2011 and my habilitation degree from the university of Koblenz-Landau (Germany) in 2016. My research focus is on formal methods of knowledge representation and artificial intelligence, both theoretical and with applications in the semantic web. I am interested in formal models of argumentation, in particular with respect to quantitative extensions, game theoretical aspects for application in multi-agent systems, the relationship of argumentation and belief revision, and inconsistency measurement. Further interests include probabilistic reasoning with incomplete and inconsistent information in propositional and first-order representations of knowledge.