Selecting and Expressing Communicative Functions in a SAIBA-Compliant Agent Framework

In SAIBA-compliant agent systems, the Function Markup Language (FML) is used to describe the agent’s communicative functions that are transformed into utterances accompanied with appropriate non-verbal behaviours. In the context of the ARIA Framework, we propose a template-based approach, grounded in the DIT++ taxonomy, as an interface between the dialogue manager (DM) and the non-verbal behaviour generation (NVBG) components of this framework. Our approach enhances our current FML-APML implementation of FML with the capability of receiving on-the-fly generated natural language and socio-emotional parameters (e.g. emotional stance) for transforming the agent’s intents in believable verbal and non-verbal behaviours in an adaptive manner.