Neuro - Symbolic AI
What Motivates Us
While deep learning and neural networks have achieved remarkable success in many domains, they often struggle to incorporate structured domain knowledge and reasoning into their predictions and outputs. This has led to growing interest in so-called neuro-symbolic AI, which aims at combining elements from both deep learning and automated reasoning, planning, and knowledge representation, in order to achieve the best of both worlds: the flexibility and data-driven performance of deep learning as well as the logical consistency and grounded reasoning of symbolic methods.
Neuro-symbolic AI approaches are ideal to exploit the knowledge of domain experts for industrial AI solutions. We develop methods that help leveraging domain experts' knowledge in a way that makes it machine-accessible (e.g., as knowledge graphs), to gain further knowledge via reasoning and prediction, and exploit knowledge for search, exploration, machine learning and problem solving.
We are particularly focusing on applications that leverage Bosch’s existing strength in industrial knowledge graphs. By integrating graph neural network approaches with industrial knowledge graphs, we can develop analytical tools that can answer queries requiring both observational data and explicit rules provided by Bosch domain experts.