Knowledge Acquisition Methods and Their Applications

Dear Colleagues,

The field of Knowledge Acquisition Methods and Their Applications is experiencing a rapid expansion, fueled by the increasing demand for autonomous intelligent systems.  Knowledge acquisition is an essential aspect of machine learning and knowledge-based systems in artificial intelligence. The aims of knowledge acquisition include decreasing the time and effort needed to solve specific problems, as well as enhancing the quality of the solutions. It involves the collection, evaluation, and interpretation of information and experiences to create and sustain knowledge within a particular domain. This knowledge enables autonomous intelligent systems to learn and adapt to their environment and make informed decisions. Knowledge acquisition methods have numerous applications in various fields, including vision, natural language processing, robotics, healthcare and finance. In these fields, knowledge acquisition methods can be used to extract information from an image, from a scientific paper or a database. In  healthcare, it can be used to collect and analyze patient data to improve diagnosis and treatment plans. In finance, it can be used to monitor and predict market trends and make investment decisions. There are numerous tasks in which corresponding applications can be found from Cybersecurity to Explainable AI. There are several methods of knowledge acquisition, including rule-based systems, artificial neural networks, decision trees and fuzzy logic systems.

Giorgos Filandrianos

Guest Editor