  Recent advances in reinforcement learning with social agents have allowed
such models to achieve human-level performance on specific interaction tasks.
However, most interactive scenarios do not have a version alone as an end goal;
instead, the social impact of these agents when interacting with humans is as
important and largely unexplored. In this regard, this work proposes a novel
reinforcement learning mechanism based on the social impact of rivalry
behavior. Our proposed model aggregates objective and social perception
mechanisms to derive a rivalry score that is used to modulate the learning of
artificial agents. To investigate our proposed model, we design an interactive
game scenario, using the Chef's Hat Card Game, and examine how the rivalry
modulation changes the agent's playing style, and how this impacts the
experience of human players in the game. Our results show that humans can
detect specific social characteristics when playing against rival agents when
compared to common agents, which directly affects the performance of the human
players in subsequent games. We conclude our work by discussing how the
different social and objective features that compose the artificial rivalry
score contribute to our results.
