The Enacted KOAN - An Agent's Knowledge of Agency

We present Knowledge Of Action Networks, which provide an enactive machine learning model for knowledge of agency in artificial intelligence. These networks, which are expected to be part of embodied intelligences existing in dynamic environments, learn to represent their environment while simultaneously learning to represent their own actions and bodies within that environment. Thus self and world are intricately coupled in their basic representations. We will also explore some of the (many) expected contributions of such networks for implementing minimal self-models, which are basic models of self-aware agents.