Probabilistic Rule Induction for Transparent CBR Under Uncertainty

CBR systems leverage past experiences to make decisions. Recently, the AI community has taken an interest in making CBR systems explainable. Logic-based frameworks make answers straightforward to explain. However, they struggle in the face of conflicting information, unlike probabilistic techniques. We show how probabilistic inductive logic programming (PILP) can be applied in CBR systems to make transparent decisions combining logic and probabilities. Then, we demonstrate how our approach can be applied in scenarios presenting uncertainty.