Current and Future Challenges in Knowledge Representation and Reasoning
It outlines key challenges and recommendations for the next decade in a foundational AI field, but is incremental as it synthesizes existing workshop discussions rather than introducing new methods.
The paper presents a manifesto from a 2022 Dagstuhl workshop, addressing the state of the art, challenges, and future priorities in Knowledge Representation and Reasoning, with a focus on its evolution and integration with areas like machine learning.
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.