  Despite recent advances in modern machine learning algorithms, the opaqueness
of their underlying mechanisms continues to be an obstacle in adoption. To
instill confidence and trust in artificial intelligence systems, Explainable
Artificial Intelligence has emerged as a response to improving modern machine
learning algorithms' explainability. Inductive Logic Programming (ILP), a
subfield of symbolic artificial intelligence, plays a promising role in
generating interpretable explanations because of its intuitive logic-driven
framework. ILP effectively leverages abductive reasoning to generate
explainable first-order clausal theories from examples and background
knowledge. However, several challenges in developing methods inspired by ILP
need to be addressed for their successful application in practice. For example,
existing ILP systems often have a vast solution space, and the induced
solutions are very sensitive to noises and disturbances. This survey paper
summarizes the recent advances in ILP and a discussion of statistical
relational learning and neural-symbolic algorithms, which offer synergistic
views to ILP. Following a critical review of the recent advances, we delineate
observed challenges and highlight potential avenues of further ILP-motivated
research toward developing self-explanatory artificial intelligence systems.
