Current and Future Challenges in Knowledge Representation and Reasoning

Knowledge Representation and Reasoning is a central, longstanding, and active area of Artificial Intelligence. Over the years it has evolved significantly; more recently it has been challenged and complemented by research in areas such as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl Perspectives workshop was held on Knowledge Representation and Reasoning. The goal of the workshop was to describe the state of the art in the field, including its relation with other areas, its shortcomings and strengths, together with recommendations for future progress. We developed this manifesto based on the presentations, panels, working groups, and discussions that took place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge Representation: its origins, goals, milestones, and current foci; its relation to other disciplines, especially to Artificial Intelligence; and on its challenges, along with key priorities for the next decade

The Role of Non-monotonic Reasoning in Future Development of Artificial Intelligence (Dagstuhl Perspectives Workshop 19072)

This report documents the program and the outcomes of Dagstuhl Perspectives Workshop 19072 "The Role of Non-monotonic Reasoning in Future Development of Artificial Intelligence". The workshop brought together researchers both from core topics and peripheral areas of non-monotonic reasoning (NMR), but also attracted researchers from other scientific domains in which recent developments have shown an increased relevance of NMR topics. The overall goal of this workshop was to reshape NMR as a core methodology for artificial intelligence being able to meet present and future challenges. Participants of this workshop discussed in what shape NMR would be useful for future AI, and how NMR can be developed for those requirements. The workshop started with brief survey talks and had some technical talks on central topics of NMR afterwards. These were followed by working groups on core aspects of NMR and potential links with learning. On the last day of the seminar, each working group presented their ideas and future plans. The workshop closed with a plenary discussion on the future of NMR