game. In the mood readings (c), each of the dots represent a neuron of the network after an action was performed. As less transparent the neuron, as higher its habituation counter, meaning it was used recently to represent a perceived confidence.[]{data-label="fig:readingsExample"}](exampleReadings.png){width="1\columnwidth"}

Assessing my own Mood
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The goal of the *Moody framework* is to provide a self-assessment, coming from the agent itself, about its own actions. The GWR will generate a temporal momentum on the confidences that will represent the agent’s performance based on its own judgment. To achieve it, we calculate the Q-values, and subsequently the confidences, of all the allowed actions over a given state. We then sum all these confidences and represent the agents’ confidence that it can reach the final state, given the cards it has at hand and the board at that moment. The last step is to convert the confidence value into a PA scale. We then train the network with the PA values for each action taken by the agent.

Different from the typical clustering applications, the GWR on the *Moody framework* is a stateful model. We obtain the mood reading by averaging all the weights of all the neurons of the network 