  The development of artificial agents for social interaction pushes to enrich
robots with social skills and knowledge about (local) social norms. One
possibility is to distinguish the expressive and the functional orders during a
human-robot interaction. The overarching aim of this work is to set a framework
to make the artificial agent socially-competent beyond dyadic
interaction-interaction in varying multi-party social situations-and beyond
individual-based user personalization, thereby enlarging the current conception
of "culturally-adaptive". The core idea is to provide the artificial agent with
the capability to handle different kinds of interactional disruptions, and
associated recovery strategies, in microsociology. The result is obtained by
classifying functional and social disruptions, and by investigating the
requirements a robot's architecture should satisfy to exploit such knowledge.
The paper also highlights how this level of competence is achieved by focusing
on just three dimensions: (i) social capability, (ii) relational role, and
(iii) proximity, leaving aside the further complexity of full-fledged
human-human interactions. Without going into technical aspects, End-to-end
Data-driven Architectures and Modular Architectures are discussed to evaluate
the degree to which they can exploit this new set of social and cultural
knowledge. Finally, a list of general requirements for such agents is proposed.
