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.

翻译：为社交互动开发的人工智能体需赋予机器人社交技能及关于（本地）社会规范的知识。一种可能性是区分人机交互中的表达秩序与功能秩序。本研究的总体目标是构建一个框架，使人工智能体在二元交互之外——即在多变的多方社交情境中——具备社交胜任能力，并超越基于个体的用户个性化，从而扩展当前“文化自适应”的概念。核心思想是为人工智能体赋予处理微观社会学中不同类型交互中断及相应恢复策略的能力。这一成果通过分类功能性与社交性中断，并探究机器人架构应满足何种要求以利用此类知识而实现。本文还强调，仅聚焦于三个维度即可达到这一胜任水平：（i）社交能力、（ii）关系角色和（iii）邻近性，从而避开完整人类交互的进一步复杂性。在不涉及技术细节的前提下，本文讨论了端到端数据驱动架构与模块化架构，以评估其开发利用这一新型社会文化知识体系的程度。最后，提出了此类智能体的一系列通用要求。