Knowledge-Based Artificial Intelligence

Abstract

Knowledge-based artificial intelligence has been extensively used in numerous application areas leading to the development of a vast number of methods and tools. In recent years, focus has shifted on non-symbolic approaches, and neural networks in particular have achieved human-level performance in various applications where accountability is a very important issue, closely related to the interpretability of artificial intelligence methods in general. Lack of interpretability of neural networks and various machine learning methods has led to the adoption of knowledge-based methods instead, which offer models compliant with explainability and interpretability requirements. In this article, an overview of knowledge-based methods is presented along with the state of the art in this area, offering to the AI practitioner guidance for applying these important methods in practice.