Abstract:

Despite the successes of recent developments in visual AI, different shortcomings still exist; from missing exact logical reasoning, to abstract generalization abilities, to understanding complex and noisy scenes. Unfortunately, existing benchmarks, were not designed to capture more than a few of these aspects. Whereas deep learning datasets focus on visually complex data but simple visual reasoning tasks, inductive logic datasets involve complex logical learning tasks, however, lack the visual component. To address this, we propose the visual logical learning dataset, V-LoL, that seamlessly combines visual and logical challenges. Notably, we introduce the first instantiation of V-LoL, V-LoL-Train, -- a visual rendition of a classic benchmark in symbolic AI, the Michalski train problem. By incorporating intricate visual scenes and flexible logical reasoning tasks within a versatile framework, V-LoL-Train provides a platform for investigating a wide range of visual logical learning challenges.

All data is released under the Creative Commons CC BY 4.0 license. All code is released under the MIT license.

Acknowledgements: This work was supported by the AI lighthouse project “SPAICER” (01MK20015E), the EU ICT-48 Network of AI Research Excellence Center “TAILOR” (EU Horizon 2020, GA No 952215), and the Collaboration Lab “AI in Construction” (AICO). The work has also benefited from the Hessian Ministry of Higher Education, Research, Science and the Arts (HMWK) cluster projects “The Third Wave of AI” and “The Adaptive Mind”, the Hessian Centre for Artificial Intelligence overall, the Hessian research priority program LOEWE within the project WhiteBox, and from the German Center for Artificial Intelligence (DFKI) project ‘SAINT’.