An integrated system for interactive continuous learning of categorical knowledge

Danijel Skoč aj, Alen Vreč ko, Marko Mahnič, Miroslav Janicek, Geert-Jan Kruijff, Marc Hanheide, Nick Hawes, Jeremy L. Wyatt, Thomas Keller, Kai Zhou, Michael Zillich, Matej Kristan

In: Journal of Experimental and Theoretical Artificial Intelligence (JETAI) 28 5 Seiten 823-848 Taylor & Francis 2016.


This article presents an integrated robot system capable of interactive learning in dialogue with a human. Such a system needs to have several competencies and must be able to process different types of representations. In this article we describe a collection of mechanisms that enable integration of heterogeneous competencies in a principled way. Central to our design is the creation of beliefs from visual and linguistic information, and the use of these beliefs for planning system behaviour to satisfy internal drives. The system is able to detect gaps in its knowledge and to plan and execute actions that provide information needed to fill these gaps. We propose a hierarchy of mechanisms which are capable of engaging in different kinds of learning interactions, e.g. those initiated by a tutor or by the system itself. We present the theory these mechanisms are build upon and an instantiation of this theory in the form of an integrated robot system. We demonstrate the operation of the system in the case of learning conceptual models of objects and their visual properties.


Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence