  Designing the decision-making processes of artificial agents that are
involved in competitive interactions is a challenging task. In a competitive
scenario, the agent does not only have a dynamic environment but also is
directly affected by the opponents' actions. Observing the Q-values of the
agent is usually a way of explaining its behavior, however, do not show the
temporal-relation between the selected actions. We address this problem by
proposing the \emph{Moody framework}. We evaluate our model by performing a
series of experiments using the competitive multiplayer Chef's Hat card game
and discuss how our model allows the agents' to obtain a holistic
representation of the competitive dynamics within the game.
