Current and Future Challenges in Knowledge Representation and Reasoning

TL;DR

This paper summarizes the state, challenges, and future directions of Knowledge Representation and Reasoning in AI, based on a 2022 Dagstuhl workshop, highlighting its evolution, current issues, and interdisciplinary relations.

Contribution

It provides a comprehensive overview and a strategic manifesto on the past, present, and future challenges and priorities in Knowledge Representation and Reasoning.

Findings

Identifies key challenges in Knowledge Representation and Reasoning.

Highlights the integration with machine learning and reasoning under uncertainty.

Recommends future research priorities for the next decade.

Abstract

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.