  Communication is essential for successful interaction. In human-robot
interaction, implicit communication enhances robots' understanding of human
needs, emotions, and intentions. This paper introduces a method to foster
implicit communication in HRI without explicitly modeling human intentions or
relying on pre-existing knowledge. Leveraging Transfer Entropy, we modulate
influence between agents in social interactions in scenarios involving either
collaboration or competition. By integrating influence into agents' rewards
within a partially observable Markov decision process, we demonstrate that
boosting influence enhances collaboration or competition performance, while
resisting influence diminishes performance. Our findings are validated through
simulations and real-world experiments with human participants.
