Cognitive Logics: Mechanisms Predicting Human Inference Patterns

September 05, 10:45 am - 12:15 pm (CEST)

Speakers: Marco Ragni, Kai Sauerwald and Gabriele Kern-Isberner

Tutorial website:

Agenda: Cognitive Logics: Mechanisms Predicting Human Inference Patterns

Systems and methods for Artificial Intelligence (AI) applicable in the real world require to represent and reason about uncertain knowledge. While this is a limitation of classical first-order logic, there is a large number of so-called non-monotonic logics, i.e., logics that aim to draw inferences only cautiously, allowing for revising them if new information becomes available. Cognitive analysis have shown that human inferential behavior can be better described employing such logics.

In this tutorial we introduce the cognitive and formal foundations of cognitive logics based on nonmonotonic logics, relevant benchmark problems from psychology that are dealt with by formal AI methods, and current challenges in modeling cognitive reasoning. The tutorial addresses a joint view on characteristics of human reasoning from the perspective of computer science and cognitive science.

Marco Ragni, Department of Computer Science, Technical Faculty, University of Freiburg, 79110 Freiburg, Germany.
Marco Ragni revceived his PhD in Artificial Intelligence in 2008 from the Technical Faculty and his PhD in Cognitive Science in 2013 in Cognitive Science from the Center for Cognitive Science at the University Freiburg.
He received his habilitations in Computer Science in 2014 and in Cognitive Science and General Psychology in 2015, and is now an Associate Professor (apl. Prof.) at the Technical Faculty of the University Freiburg and a DFG-Heisenbergfellow. His research interests focus on computational models of high-level cognition, both from a cognitive, computational, and neuroscience perspective.

Gabriele Kern-Isberner, Department of Computer Science, TU Dortmund, 44227 Dortmund, Germany.
Gabriele Kern-Isberner received her diploma in mathematics in 1979, and her doctoral degree in mathematics in 1985, both from the University of Dortmund. In 2000, she did her habilitation in computer science at the FernUniversität in Hagen, the German Open University, and got the Venia legendi for computer science. She worked as a research assistant and as a lecturer at the universities of Dortmund, Hagen, and Leipzig. Since 2004, she has been a Professor for Information Engineering at the department of computer science at the University of Technology Dortmund.
Her scientific work focuses on qualitative and quantitative approaches to knowledge representation such as default and non-monotonic logics, uncertain reasoning, belief revision, and argumentation. Her research interests include in particular the development of methods that help integrate approaches from different fields, such as the combination of first-order logic and probabilities, or building bridges between uncertain reasoning and learning. Some of her works also deal with the cognitive aspects of formal reasoning models. She has been involved in the organization of major conferences in AI, was co-chair of ECSQARU 2019, was co-chair of FoIKS 2020, and she currently co-chairs the steering committee of NMR workshops.

Kai Sauerwald, Knowledge Based Systems, Faculty of Mathematics and Computer Science, FernUniversität in Hagen, 58097 Hagen, Germany.
Kai Sauerwald recived his master degree in computer science from the TU Dortmund, and is currently a resarcher at the Knowledge Base Systems group at the FernUniversität in Hagen. Recently he was one of the two local-organisers of FoIKS 2020.
His research interests focus on belief change and its applications and connections to other areas from knowledge representation and psychology.