Information for real-life AI applications is usually pervaded by uncertainty and subject to change, thus demands non-classical reasoning approaches. At the same time, psychological findings indicate that human reasoning cannot be completely described by classical logical systems. Sources of explanations are incomplete knowledge, incorrect beliefs, or inconsistencies. A wide range of reasoning mechanisms has to be considered, such as analogical or defeasible reasoning, possibly in combination with machine learning methods. The field of knowledge representation and reasoning ofers a rich palette of methods for uncertain reasoning both to describe human reasoning and to model AI approaches. The aim of this series of workshops is to address recent challenges and to present novel approaches to uncertain reasoning and belief change in their broad senses, and in particular provide a forum for research work linking diferent paradigms of reasoning. A special focus is on papers that provide a base for connecting formal-logical models of knowledge representation and cognitive models of reasoning and learning, addressing formal and experimental or heuristic issues. Previous events of the Workshop on ”Formal and Cognitive Reasoning” and joint workshops took place in Dresden (2015), Bremen (2016), Dortmund (2017), Berlin (2018), Kassel (2019), Bamberg (2020, online), Berlin (2021, online), and Trier (2022, online).
Acknowledgments