  This study investigated how social interaction among robotic agents changes
dynamically depending on individual sense of agency. In a set of simulation
studies, we examine dyadic imitative interactions of robots using a variational
recurrent neural network model. The model is based on the free energy principle
such that interacting robots find themselves in a loop, attempting to predict
and infer each other's actions using active inference. We examined how
regulating the complexity term to minimize free energy during training
determines the dynamic characteristics of networks and affects dyadic imitative
interactions. Our simulation results show that through softer regulation of the
complexity term, a robot with stronger agency develops and dominates its
counterpart developed with weaker agency through tighter regulation. When two
robots are trained with equally soft regulation, both generate individual
intended behavior patterns, ignoring each other. We argue that primary
intersubjectivity does develop in dyadic robotic interactions.
