last modified: 21 Dec 2020
Over the last two decades, Machine Learning (ML) has made incredible progress and become very effective at solving specific tasks while being robust across many experimental learning applications. Deep learning, statistical (relational) learning, reinforcement learning and logic-based and/or probabilistic learning are among the many ML approaches that are witnessing such advancements. On the other hand, Knowledge Representation and Reasoning (KR) has continued to be at the core of Artificial Intelligence (AI) research providing solutions for explicit declarative representation of knowledge and knowledge-based inference, which have theoretical and practical relevance in many aspects of AI as well as in new emerging fields outside AI. The synergy between these two areas of AI has the potential to lead to new advancements on the foundations of AI that offer novel insights into open fundamental challenges including, but not limited to, learning symbolic generalisations from raw (multi-modal) data, using knowledge to facilitate data-efficient learning, supporting interpretability of learned outcomes, federated multi-agent learning and decision making.
This year, for the second time, KR2021 will host a special session on "Knowledge Representation and Machine Learning". This special session aims at providing researchers and industrial practitioners with a dedicated forum for presentation and discussion of new ideas, research experience and emerging results on topics related to computational learning and symbolic knowledge representation and reasoning. This special session provides the opportunity for fostering meaningful connections between researchers from these two main areas of AI and, at the same time, offering the possibility to learn about progress made on these topics, share their own views and learn about approaches that could lead to effective cross-fertilisation among research in ML and KR and new innovative solutions to key AI research challenges.
The Special Session on KR and ML at KR2021 invites submissions of papers across KR and ML on advancements in one of these areas for the purpose of addressing open research challenges in the other, integration of computational learning and knowledge representation and reasoning, and the application of combined KR and ML approaches to solve real-world problems, including case studies and benchmarks.
We welcome papers on a wide range of topics, including but not limited to:
The special session emphasizes KR and ML, and welcomes contributions that extend the state of the art at the intersection of KR and ML. Therefore, KR-only or ML-only submissions will not be accepted for evaluation in this special session.
Submissions will be rigorously peer reviewed by PC members who are active in KR and ML. Submissions will be evaluated on the basis of the overall quality of their technical contribution, including criteria such as originality, soundness, relevance, significance, quality of presentation, and understanding of the state of the art.
In this special session, the selection process of the highest quality papers will apply the following criteria:
Vaishak Belle (University of Edinburgh, UK) vaishak
Luc De Raedt (KU Leuven, Belgium) luc.deraedt @cs.kuleuven.be