Artificial agents might not understand human interests and actions if these agents cannot anticipate how a person understands a situation and, based on this, what could be his/her expectations. In many cases, understanding, expectations and behaviors are constrained, if not driven, by culture. Can we provide human culture to an artificial agent? Can we provide formal representations of different cultures? In this paper we discuss the (elusive) notion of culture and propose an approach based on the notion of trait which, we argue, allows building formal modules suitable to represent culture (broadly understood). We distinguish the trait types (knowledge, rule, behavior, interpretation) that such modules should contain and briefly discuss how they could be organized. Finally, we exemplify the role of a trait module in the flow of information internal to an agent highlighting surprising potentialities.
Trait-based Culture and its Organization: Developing a Culture Enabler for Artificial Agents
Contributo in volume
IEEE, New York, USA
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 333–338. New York: IEEE, 2018
info:cnr-pdr/source/autori:Borgo, Stefano; Blanzieri, Enrico/titolo:Trait-based Culture and its Organization: Developing a Culture Enabler for Artificial Agents/titolo_volume:2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)/