Causality Prediction with Neural-Symbolic Systems: A Case Study in Smart Grids

Abstract

In complex systems, such as smart grids, explanations of system events benefit both system operators and users. Deriving causality knowledge as a basis for explanations has been addressed with rule-based, symbolic AI systems. However, these systems are limited in their scope to discovering causalities that can be inferred by their rule base. To address this gap, we propose a neural-symbolic architecture that augments symbolic approaches with sub-symbolic components, in order to broaden the scope of the identified causalities. Concretely, we use Knowledge Graph Embeddings (KGE) to solve causality knowledge derivation as a link prediction problem. Experimental results show that the neural-symbolic approach can predict causality knowledge with a good performance and has the potential to predict causalities that were not present in the symbolic system, thus broadening the causality knowledge scope of symbolic approaches.