Object Identity Reloaded—A Comprehensive Reference for an Efficient and Effective Framework for Logic-Based Machine Learning

Sub-symbolic Machine Learning (ML) techniques, and specifically Neural Network-based ones, recently took over the research landscape, thanks to their efficiency and impressive effectiveness. On the other hand, the recent debate on ethics and AI and the first regulations on AI are progressively calling for anthropocentricity, which in turn requires explicit, human-understandable, and explainable approaches and representations that allow humans to be active parts in the loop. In these cases, logic-based approaches are more suitable. The Inductive Logic Programming (ILP) branch of research in ML provides an anwer to this need and a uniform and unifying framework for three relevant industrial and research concerns: management of databases, implementation of software systems, and modeling of human-like reasoning strategies. A particular ILP framework based on the Object Identity (OI) assumption was proposed in the 1990s, for which desirable theoretical and pratical properties were demonstrated and working tools and systems that successfully approached real-world and classical problems in AI were developed. In an age when mainstream research and media seem to reduce AI and ML to just deep learning, this paper celebrates the 30th anniversary of OI by providing for the first time a comprehensive overview of the framework to be used as a reference for researchers still interested in investigating the ILP approach to ML.