Automatic Knowledge Discovery and Evolution

Knowledge Discovery and Evolution (KDE) is of interest to a broad array of researchers from both Philosophy of Science (PoS) and Artificial Intelligence (AI), particularly, Knowledge Representation and Reasoning (KR), Machine Learning and Data Mining (ML-DM) and the Agent Based Systems (ABS) communities. In PoS, Haig recently proposed a so-called broad theory of scientific method that uses abduction for generating theories to explain phenomena. He refers to this method of scientific inquiry as the Abductive Theory of Method (ATOM). We analyse ATOM, align it with KR and ML-DM perspectives and propose an algorithm and an ontology for supporting agent based KDE based on ATOM. We illustrate the use of the algorithm and the KDE ontology on a use case application for electricity consumption behaviour in residential households.