Dagstuhl Perspectives Workshop 21092
Current and Future Challenges in Knowledge Representation and Reasoning Postponed
( Feb 28 – Mar 05, 2021 )
- James P. Delgrande (Simon Fraser University - Burnaby, CA)
- Birte Glimm (Universität Ulm, DE)
- Thomas Meyer (University of Cape Town, ZA)
- Miroslaw Truszczynski (University of Kentucky - Lexington, US)
- Frank Wolter (University of Liverpool, GB)
- Shida Kunz (for scientific matters)
- Jutka Gasiorowski (for administrative matters)
Knowledge Representation and Reasoning (KR) is the field of Artificial Intelligence (AI) that deals with explicit, declarative representations of knowledge along with inference procedures for deriving further, implicit information from this knowledge. It has evolved significantly over the last 40 years, and research in many subareas of KR has matured from the exploration of foundations, to the development and analysis of systems for emerging or established applications. However, while progress in KR has been steady and often impressive, it has not kept pace with the recent successes in AI in the use of statistical techniques and machine learning. Indeed, much of the work and focus in AI has shifted to machine learning and statistical applications in areas like vision, natural language understanding, and big data. Nonetheless, we take it as given that KR is an essential area of AI, and that research and development in KR remains necessary for any ultimate, general theory of intelligence.
With this belief as a key motivation, this Dagstuhl Perspectives Workshop aims to assess the current state of KR along with future trends and developments, and to develop an innovative agenda for the next 20 years of KR research. Among its goals are identifying areas for emphasis, assessing prospects for practical application of techniques, and considering how KR may address limitations of statistical techniques and machine learning. Because KR is strongly interdisciplinary, another important goal is to develop strategies for fostering links between KR and other areas of AI and computing science.
Tentatively, the workshop will begin with introductory and overview talks on select topics, followed by short position statements by participants. A major part of the workshop will be given over to working groups, reports from these groups and general discussion. The final morning will discuss meeting outcomes, next steps, and planning for the future. The workshop will be described in a Dagstuhl Report, as with all seminars. Two further outcomes will be a Dagstuhl Manifesto that will describe the suggested initiatives and goals resulting from the meeting, as well as a more focused and technical document written for researchers in the field.
A key condition of success for the workshop is that the participants reflect the diversity of the field, represent all its main areas of research, offer balance between theory and practice of KR, and come from a broad range of geographical areas. As an additional prerequisite for success, the workshop seeks to attract participants from areas adjacent to KR, such as natural language understanding, planning, and constraints, to learn about their views on KR and on effective ways KR and their areas may develop critical synergies.
The attendance in the workshop is limited. Therefore, active participation and direct contributions from all attendees are necessary for the workshop’s success. To create an atmosphere of creativity and a sense of a shared vision, all participants are expected to attend for the full duration of the workshop, to contribute during the workshop, and to get involved in the preparation of workshop documents once the meeting is over.
- Artificial Intelligence
- Logic in Computer Science
- Symbolic Computation
- knowledge representation and reasoning
- declarative representations
- formal logic
- applications of logics