Learning to Learn: Dynamic Runtime Exploitation of Various Knowledge Sources and Machine Learning Paradigms

Abstract

The ability to learn at runtime is a fundamental prerequisite for self-adaptive and self-organising systems that allows for dealing with unanticipated conditions and dynamic environments. Often, this machine learning process has to be highly or fully autonomous. That is, the degree of interaction with humans must be reduced to a minimum. In principle, there exist various learning paradigms for this task such as transductive learning, reinforcement learning, collaborative learning, or - if interaction with humans is allowed but has to be efficient - active learning. These paradigms are based on different knowledge sources such as appropriate sensor measurements, humans, or databases as well as access models considering e.g., availability or reliability. In this article, we propose a novel meta learning approach that aims at dynamically exploiting various possible combinations of knowledge sources and machine learning paradigms at runtime. The approach is learning in the sense that it self-optimises a certain objective function (e.g., it maximises a classification accuracy) at runtime. We present an architectural concept for this learning scheme, discuss some possible use cases to highlight the benefits, and derive a research agenda for future work in this field.