AI research over the past few decades has highlighted the strengths and weaknesses of both symbolic and machine learning approaches to AI, with some weaknesses in one being strengths in the other. Much is also known about the fragmentation of the field along these two approaches. However, recent discussions in AI have highlighted the need to integrate both symbolic and sub-symbolic methods, in a hybrid approach to AI, to create novel techniques that leverage both reasoning and learning. This workshop seeks to contribute to this discussion by exploring the following:

• a systems approach to AI: Modular AI architectures that allow components dedicated to specific functionality, such as reasoning, learning, and other cognitive capabilities to work together in a complementary way. How should such an architecture look like, especially when the solution to the problem is not end-to-end differentiable in a way that purely neural network architectures can solve, or purely symbolic?
• the use of composable AI components: What new techniques can allow new AI systems to be composed from existing AI modules?
• leveraging the best of both symbolic and sub-symbolic techniques in hybrid reasoning and learning architectures: What novel architectures are needed to take advantage of advances in knowledge representation, reasoning and learning without having to reinvent the wheel in one system or the other?

To achieve the above, there is the need to explore novel knowledge representation techniques that allow the seamless flow of computations between symbolic and sub-symbolic AI components. There is also the need to develop new datasets that evaluate the capabilities highlighted above, especially focusing on problems that cannot be solved by end-to-end differentiable neural network architectures or purely symbolic reasoning methods alone.

Research areas of interests include:

• Knowledge representation for hybrid AI architectures
• Hybrid AI architectures for reasoning and learning
• Explaining decisions in hybrid AI systems
• Composable AI architectures
• Machine learning for reasoning
• Augmenting machine learning with reasoning
• Probabilistic reasoning and learning
• Question answering through learning and reasoning
• Commonsense reasoning
• Learning from small datasets
• Knowledge graphs enabled learning and reasoning

The one-day workshop will include invited talks, discussions, and presentations of accepted papers and posters.

We welcome submissions of long papers (KR 2021 style, up to 9 pages excluding references), short position papers (KR 2021 style, up to 4 pages excluding references), or posters describing new, previously unpublished research in this field. Authors may also submit already published work (verbatim with no page limit), citing the original work. Papers must be submitted as PDF files via EasyChair: https://easychair.org/conferences/?conf=krhcaikr2021.
Accepted papers will be presented as a short talk during the workshop and may be published in supplementary proceedings for the workshop.

• Submission of title and abstract: June 25, 2021
• Workshop paper submission deadline: July 2, 2021 July 9, 2021
• Workshop paper notification: August 6, 2021
• Workshop registration deadline: TBA
• Workshop dates: Wednesday November 3, 2021, UTC 08:30 - 15:00

• Kwabena Nuamah (University of Edinburgh) (Email: k.nuamah [at] ed [dot] ac [dot] uk )
• Efi Tsamoura (Samsung AI Research)
• Pavan Kapanipathi (IBM Research)
• Jeff Pan (University of Edinburgh)

• Massimiliano Patacchiola (University of Cambridge)
• Paolo Pareti (University of Winchester)
• Pavlos Andreadis (University of Edinburgh
• Ibrahim Abdelaziz (IBM Research)