Lexicon formation in autonomous robots

“The meaning of a word is its use in the language”. In the first half of the 20th century Ludwig Wittgenstein introduced this idea into philosophy and especially in the last few decades, related disciplines such as psychology and linguistics started embracing the view that that natural language is a dynamic system of arbitrary and culturally learnt conventions. From the end of the nineties on, researchers around Luc Steels transferred this notion of communication to the field of artificial intelligence by letting software agents and later robots play so-called language games in order to self-organize communication systems without requiring prior linguistic or conceptual knowledge. Continuing and advancing that research, the work presented in this thesis investigates lexicon formation in humanoid robots, i.e. the emergence of shared lexical knowledge in populations of robotic agents. Central to this is the concept of referential uncertainty, which is the difficulty of guessing a previously unknown word from the context. First in a simulated environments and later with physical robots, this work starts from very simple lexicon formation models and then systematically analyzes how an increasing complexity in communicative interactions leads to an increasing complexity of representations and learning mechanisms. We evaluate lexicon formation models with respect to their robustness, scaling and their applicability to robotic interaction scenarios and one result of this work is that the predominating approaches in the literature do not scale well and are not able to cope with the challenges stemming from grounding words in the real-world perceptions of physical robots. In order to overcome these limitations, we present an alternative lexicon formation model and evaluate its performance.