Modelling context awareness for a situated semantic agent

This paper presents a model of contextual awareness implemented for a social communicative robot Leolani. Our model starts from the assumption that robots and humans need to establish a common ground about the world they share. This is not trivial as robots make many errors and start with little knowledge. As such, the context in which communication takes place can both help and complicate the interaction: if the context is interpreted correctly it helps in disambiguating the signals, but if it is interpreted wrongly it may distort interpretation. We defined the surrounding world as a spatial context, the communication as a discourse context and the interaction as a social context, which are all three interconnected and have an impact on each other. We model the result of the interpretations as symbolic knowledge (RDF) in a triple store to reason over the result, detect conflicts, uncertainty and gaps. We explain how our model tries to combine the contexts and the signal interpretation and we mention future directions of research to improve this complex process.