Cognitive Logics
Formal and Cognitive Methods for Reasoning in an Uncertain and Dynamic World

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.

The tutorial will be held at the 17th International Conference on Principles of Knowledge Representation and Reasoning (KR 2020), which takes place on September 12-18th in Rhodes, Greece.

Presenters: Gabriele Kern-Isberner and Marco Ragni
Length of the Tutorial: 1/2 day (90 + 90 minutes)


Outline of the Tutorial

Topics to be dealt with in the tutorial:

Target audience and prerequisite knowledge

Any AI researcher interested in the fields of knowledge representation and reasoning, data science, logic, uncertain reasoning, commonsense reasoning, and their relationships to cognition and cognitive psychology may benefit from this tutorial.
Only basic knowledge of propositional and first-order logic is required to follow the tutorial.


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 FernUniversitaet 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.
Marco Ragni, Danish Institute of Advanced Study, South Denmark University, Denmark and
Cognitive Computation Lab, 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.