XAI-MEG: Combining symbolic AI and machine learning to generate first-principles models and causal explanations

Current machine learning methods generally do not reveal any mechanistic insights or provide causal explanations for their decisions. While this may not be a big concern in typical computer vision, game playing, and recommendation systems, this is important for many problems in chemical engineering such as fault diagnosis, process control, and process safety analysis. To address these drawbacks, one needs to go beyond purely data-driven machine learning techniques and incorporate the lessons learned from the expert systems era of artificial intelligence (AI), in the 1970s and 1980s. In this article, we present such a hybrid-AI framework that demonstrates how symbolic AI techniques can be integrated with numeric AI-based machine learning methods.

Automated summary

Current machine learning methods lack mechanistic insights and causal explanations, which is crucial for problems in chemical engineering. A hybrid-AI framework that combines symbolic AI techniques with numeric machine learning methods is proposed to address this limitation.