Smart IoT systems are increasingly required to take decisions and act in contexts that are only partially known, or that dynamically evolve through time. Therefore, they should become able to to autonomously learn models of their context, there included a model of the effects of their own actions on it (that is, developing a 'sense of agency'). This would enable them to learn how to act purposefully towards the achievement of specific goals. In this paper we propose a general-purpose Bayesian learning approach to build such context models and the associated sense of agency, and present some promising preliminary experiments performed in a smart home scenario.
Developing a 'Sense of Agency' in IoT Systems: Preliminary Experiments in a Smart Home Scenario / Lippi, M.; Mariani, S.; Zambonelli, F.. - (2021), pp. 44-49. (Intervento presentato al convegno 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2021 tenutosi a deu nel 2021) [10.1109/PerComWorkshops51409.2021.9431003].