for the impact of selected actions and translate it into an pleasure/arousal representation [@costa1996mood] of the agent’s mood. Our method builds on the introspective transformation from the Q-values [@cruz2020explainable], and introduces a Growing-When-Required (GWR) network to transform confidence readings into the pleasure/arousal scale, and to create strong inter-action correlation, which directly maps the action-selection-mood triplet in our competitive card game scenario and carries a temporal momentum that we use to explain the agents’ performance while the game happens.

We also investigate the *Moody framework* as a tool for explaining the agents’ behavior under a competitive perspective. The intrinsic mood readings are directly formulated by an agent assessing its own actions. In a competitive scenario, the actions of the agent’s opponents impact directly the agents’ own mood. Thus, being informed on how each agent perceives their opponent’s actions can give us a much richer explanation of the performance of the agent while the game happens.

In this paper, we formalize the entire *Moody framework*, and aim on addressing two main research questions: 1) How the mood readings provide the understanding of the agents’ behavior on the Chef’s Hat game; 2) How close is the representation of the mood estimated by an 